![Percentage of new WT installations in 2020 (both on-and offshore), in terms of produced MW capacity. Based on data retrieved from [14].](https://www.researchgate.net/publication/358758589/figure/fig1/AS:1127623472222208@1645857794177/Percentage-of-new-WT-installations-in-2020-both-on-and-offshore-in-terms-of-produced_Q320.jpg)
![Estimated costs of a HAWT, as a percentage of the total and excluding foundations. Based on data retrieved from Ref. [30].](https://www.researchgate.net/publication/358758589/figure/fig2/AS:1127623472234496@1645857794206/Estimated-costs-of-a-HAWT-as-a-percentage-of-the-total-and-excluding-foundations-Based_Q320.jpg)
![Damage and failure statistics. (a) Percentage of unforeseen malfunctions as recorded in Germany for 1500 wind turbines. Based on data retrieved from Ref. [42], collected over 15 years (34,582 events). (b,c) percentage distribution of the total number of failures and downtime for WTs. Based on data retrieved from Ref. [43], collected from several sources in Sweden, totalling about 600 WTs from 2000 to 2004 (1202 events, 156,202 h). The nomenclature used in the original sources is reproduced for all charts.](https://www.researchgate.net/publication/358758589/figure/fig5/AS:1127623472234498@1645857794321/Damage-and-failure-statistics-a-Percentage-of-unforeseen-malfunctions-as-recorded-in_Q320.jpg)
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Citation: Civera, M.; Surace, C.
Non-Destructive Techniques for the
Condition and Structural Health
Monitoring of Wind Turbines: A
Literature Review of the Last 20
Years. Sensors 2022,22, 1627.
https://doi.org/10.3390/s22041627
Academic Editors: Adam Glowacz,
Jose A Antonino-Daviu and
Wahyu Caesarendra
Received: 29 December 2021
Accepted: 15 February 2022
Published: 18 February 2022
Publisher’s Note: MDPI stays neutral
with regard to jurisdictional claims in
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iations.
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
sensors
Review
Non-Destructive Techniques for the Condition and Structural
Health Monitoring of Wind Turbines: A Literature Review of
the Last 20 Years
Marco Civera * and Cecilia Surace
Department of Structural, Geotechnical and Building Engineering (DISEG), Politecnico di Torino, Corso Duca
degli Abruzzi 24, 10129 Turin, Italy; cecilia.surace@polito.it
*Correspondence: marco.civera@polito.it
Abstract:
A complete surveillance strategy for wind turbines requires both the condition monitoring
(CM) of their mechanical components and the structural health monitoring (SHM) of their load-
bearing structural elements (foundations, tower, and blades). Therefore, it spans both the civil
and mechanical engineering fields. Several traditional and advanced non-destructive techniques
(NDTs) have been proposed for both areas of application throughout the last years. These include
visual inspection (VI), acoustic emissions (AEs), ultrasonic testing (UT), infrared thermography (IRT),
radiographic testing (RT), electromagnetic testing (ET), oil monitoring, and many other methods.
These NDTs can be performed by human personnel, robots, or unmanned aerial vehicles (UAVs);
they can also be applied both for isolated wind turbines or systematically for whole onshore or
offshore wind farms. These non-destructive approaches have been extensively reviewed here; more
than 300 scientific articles, technical reports, and other documents are included in this review,
encompassing all the main aspects of these survey strategies. Particular attention was dedicated
to the latest developments in the last two decades (2000–2021). Highly influential research works,
which received major attention from the scientific community, are highlighted and commented upon.
Furthermore, for each strategy, a selection of relevant applications is reported by way of example,
including newer and less developed strategies as well.
Keywords:
structural health monitoring; condition monitoring; damage detection; fault diagnostics;
non-destructive testing; artificial intelligence; wind turbine; wind farm; blade monitoring
1. Introduction
There is a general consensus from technicians, political leaders, and public opinion
alike that the worldwide energy sector should shift to more sustainable sources.
Wind power is widely considered one of the best options in this sense. As for any
energy source, it has its own advantages and limitations; for instance, it is an intermittent
source, not dispatchable on demand but rather subject to the fluctuating nature of meteoro-
logical conditions. Nevertheless, it is fully renewable and highly sustainable, with minimal
environmental impact when compared to traditional fuel power. However, wind turbines
(WTs) come with both worker and public safety concerns.
In case of mechanical faults, turbine nacelle fires may erupt. Due to their height,
these can be dangerous to extinguish, while releasing toxic flumes and potentially causing
secondary fires in their immediate surroundings.
The risks are even more evident for structural collapses. These can be due to global or
local failure mechanisms. The first case can be caused by a failure at any point along the
tower height or its complete toppling due to foundation issues. In this instance, not only
the wind turbine but also nearby structures can be damaged in the collision.
Even in the case of local failures, the consequences may be particularly severe, espe-
cially for turbines located near highly-populated areas. These failures include the detach-
Sensors 2022,22, 1627. https://doi.org/10.3390/s22041627 https://www.mdpi.com/journal/sensors
Sensors 2022,22, 1627 2 of 52
ment of the rotor from the nacelle or of the whole rotor–nacelle ensemble from the tower.
However, the most well-known structural risk concerns blade brakes.
Indeed, according to Ref. [
1
], blade failure is the most common risk, accounting for
∼
65% of all incidents (when the detachment of both the full blade or part thereof are
accounted for together). Even the loss of some smaller blade components may endanger
people’s safety, due to their potential high impact velocity and the long distances they
can cover when carried by strong winds. For this reason, WT blade monitoring will be
particularly detailed in this discussion.
Thus, wind turbines require constant integrity and safety monitoring. This can be
achieved with automated approaches, based on Artificial Intelligence (AI). However, these
AI-based diagnostics strategies require damage-sensitive features for data-driven anomaly
detection. These should ideally be retrieved from the operating WT in a non-destructive,
non-invasive fashion.
To this aim, this paper reports a broad overview of non-destructive evaluation (NDE)
approaches and the respective techniques (NDTs) for structural health monitoring (SHM)
and condition monitoring (CM). Due to the very large quantity of published documents on
this subject, vibration- and SCADA-based approaches will not be included here; instead,
they will be covered in a dedicated review in the near future.
The remainder of this paper is structured as follows. Section 2describes the historical
and current context of the WT and wind industry. Section 3recalls the main components
of any generic wind turbine and discusses their implicit risks and the main causes of
damage/failure. Section 4briefly describes the main sub-fields of applications for both
CM and SHM in WTs. Section 5lists the main NDE strategies for the structural and
mechanical components. Section 6reports some final discussions and suggestions. Finally,
the conclusions end this paper.
2. Context: The Worldwide Politics and Economics of Wind Turbines
2.1. Climate Change and the Political Stance on Sustainable Energy Sources
The need for sustainable and renewable energy sources (also known as green energy)
originated mainly in the last decades. This was due to the raising concerns about the mid-
and long-term effects of human activities on the environment, on a global scale.
For this reason, in 1980 the World Meteorological Organization (WMO) organized the
first world conference on climate “to prevent potential man-made changes in climate that
might be adverse to the well-being of humanity” [2].
In the following years and up to the present day, numerous UN climate conferences
and meetings have been held. Among these, the 1997 UNFCCC Climate Change Confer-
ences resulted in the famous Kyoto Protocol, where binding milestones were set for the
reduction in harmful emissions by industrialized countries. In 2015, these meetings led
to the Paris Agreement, where 196 countries agreed to the goal of limiting the increase in
the global temperature to less than two Celsius degrees above pre-industrial levels. The
most recent UN conference on climate, named COP26, was ongoing at the time of writing.
Therefore, there is pressure from both public opinion and decision-makers to subsidize
green energy producers to extend their market share.
In this regard, the International Renewable Energy Agency (IRENA) recently published
in its latest annual report [
3
] the statistics on the global renewable energy generation capac-
ity. According to the data collected, the global renewable generation capacity amounted
to 2799 GW at the end of 2020. In the same year, wind energy took second place with a
capacity of 733 GW (26%), preceded only by hydropower sources (1332 GW or 48%) and
ahead of solar energy (714 GW or 25%, including both photovoltaic and concentrated solar
power). In more detail, onshore WTs and wind farms produced 699 GW, almost on par with
the total solar energy production by itself, while offshore sites added a further ∼34 GW.
Furthermore, solar and wind energy continued to dominate the expansion of renew-
able capacity. Out of a total increase in the total renewable generation capacity by 261 GW
(+10.3%) in 2020, solar energy continued to drive capacity expansion, with an increase of
Sensors 2022,22, 1627 3 of 52
127 GW (+22%), yet very closely followed by wind energy with 111 GW (+18%). Indeed,
in most industrialized countries, due to the limited possibility of new large hydroelectric
installations, the increased demand for renewable energy is largely covered by these two
sources, with wind energy production exceeding its solar counterpart in many European
countries, e.g., Italy, according to the most recent data [
4
]. From a historical perspective
in the European Union, the wind power generation capacity has been well above solar
photovoltaic capacity since the early 2000s, overtaking the fuel oil capacity in 2007, nuclear
energy in 2013, hydroelectric in 2015, and coal in 2016, remaining only behind natural gas
as of 2017 [5].
2.2. The Current and Near-Future Economic Impact of Wind Power
At the world scale, according to the latest data from BloombergNEF, wind power
developers around the world commissioned a record 96.7 GW of installations in 2020, up
59% from 60.7 GW installed in 2019. This increase in capacity was mainly due to the surge
in installations in China and the United States [
6
]. Specifically, 2020 set a record year of
wind growth for China, with a 36% year-on-year increase in WT installations [
7
]. In the
same year, the USA installed a record 14.2 GW, more than in any other year so far [
8
].
Other non-European high-GDP countries strongly committed to the transition towards
wind energy include Japan (where the Japan Wind Power Association declared 2020 as the
best year for capacity addition in the country’s history [9]), Australia (where wind energy
supplied 35.9% of the clean energy in 2020, remaining the leading renewable source and
setting a record-breaking year [
10
]), India (with a year-on-year increase of +5.9%, reaching
a total of 37.7 GW installed nationwide [
11
]), Brazil (where the installed capacity increased
from 1 to 18 GW from 2010 to 2020 [
12
]), and South Korea (where the government Green
New Deal, announced in July 2020, set a goal of 12 GW of wind capacity by 2030, also
announcing the largest offshore wind farm in the world, to be built in the South Jeolla
province [13]).
At the European scale, the 5-year outlook reported in Ref. [
14
] considered a growth
of 15 GW p.a. as a “realistic expectation”. It should be stated, furthermore, that new
installations (as of 2020) of offshore WTs strongly exceeded onshore ones in many North
Sea countries (1493 MW vs. 486 MW in the Netherlands, 706 MW vs. 152 MW in Belgium,
and 483 MW vs. 115 MW in the UK [
14
]); the aggregate onshore and offshore installations
are portrayed in Figure 1. This makes economic sense due to the more stable and steady
wind flows in the open sea and the lower acoustic and visual impact in comparison to their
onshore counterparts. However, with the operation and maintenance (O&M) costs much
larger for offshore production facilities, structural health and condition monitoring will be
even more important.
Figure 1.
Percentage of new WT installations in 2020 (both on- and offshore), in terms of produced
MW capacity. Based on data retrieved from [14].
Sensors 2022,22, 1627 4 of 52
In Mediterranean and southern European countries, this can act as a driving economic
force for less-developed areas. For instance, Italy was the fifth country in Europe in terms
of installed wind capacity, with more than 10 thousand MW of plants installed as of 2019
(mostly onshore), for a total of about 670 WTs [
15
]. Most of the wind farms (over 90%)
are concentrated in the South and the islands of Sicily and Sardinia, due to the greater
availability in these regions of adequately windy sites. At the moment, not even 1 MW
of offshore installations are operative. However, the installation of the first offshore wind
farm in Italy (in the Sicilian channel) is expected in the coming years.
The prospects in the medium to long term are very positive, especially due to the
unique opportunity given by the Recovery Plan for Europe. According to the National
Integrated Plan for Energy and Climate (Piano Nazionale Integrato per l’Energia e il Clima,
PNIEC [
16
])], the installed wind energy capacity in Italy should reach approximately
19,300 MW by 2030, of which approximately 900 MW will come from offshore wind. This
would guarantee an annual production of electricity equal to 40 TWh, which is 10% of the
national gross electricity consumption.
2.3. Expected Returns and Benefits from WT Monitoring
For all the reasons discussed above, the impact of CM and SHM cannot be neglected
from the perspective of more reliable, more cost-effective wind power production in the
coming decades. From a risk management point of view, the NDE strategies and their
related NDTs are required to reduce the number of both minor incidents and (fatal or non-
fatal) severe accidents. From the economic perspective, the same approaches are intended to
improve, rationalize, and automate as much as possible the maintenance routine. In general
terms, the end goal is a paradigm shift from time-scheduled maintenance to condition-based
maintenance, which is more flexible and avoids unnecessary deployments on-site. This
can save man-hours and transportation costs. Fewer human operators on-site would also
mean less exposure to dangerous conditions (especially for offshore wind farms). All these
beneficial consequences would lower the O&M costs and therefore the total energy price,
thus making the renewable energy from wind farms more cost-competitive in comparison
to other traditional alternatives, such as fossil fuels. In this sense, the cost-efficiency of WTs
is commonly evaluated in terms of (yearly or life-long) levelized cost of energy (LCOE),
i.e., the total cost (per year or over lifetime) divided by the (annual or lifetime) energy
production [
17
,
18
]. Decreasing O&M costs automatically increases the LCOE of new and
already-existing installations [19].
In particular, for mechanical faults, the mean downtime can vary in a range from
6 to 15 days onshore. The cost of a gearbox bearing exchange ranges from EUR 15000
for a simple up-tower replacement to more than EUR 1 million for the substitution of
larger (5 MW) gearboxes [
20
]. These figures are indicative of onshore installations; as
mentioned earlier, failures in offshore WTs would result in even longer interruptions and
costlier reparations [
21
]. McMillian & Ault [
22
] provided a detailed quantitative analysis of
the impact of condition monitoring on O&M costs. In this regard, Figure 2reports some
indicative estimates.
Regarding (partial or total) structural collapse, while likely much rarer, this occurrence
would result in the complete loss of the whole asset, plus the collateral damage; therefore,
an SHM apparatus should be always considered jointly to the more common CM systems.
For all these applications, the expected gains of implementing an SHM/CM strategy
can be evaluated in terms of its value of information (VoI [
23
]). Some examples can be found
in Ref. [
24
] for the SHM of WT blades and in Refs. [
25
,
26
] for the CM of WT gearboxes and
generators (in the same order).
Sensors 2022,22, 1627 5 of 52
Figure 2.
Bar chart of the estimated investment (
left
) and O&M (
right
) costs per MW of an onshore
HAWT, according to the class of mean electric power produced. Based on data retrieved from Ref. [
27
].
3. Wind Turbines: Structural and Mechanical Components
There are several different typologies of “wind turbines”, the most common type
being the so-called Horizontal Axis Wind Turbine (HAWT) systems. In this configuration,
the rotation axis of the rotor is parallel to the ground. Specific attention must be paid to
the orientation with respect to the wind direction, which is different from other types of
wind turbines such as those with a vertical axis, whose orientation is independent of the
prevailing wind direction. The upwind configuration is the most common one, with the
rotor facing the incoming wind.
In this review, only HAWT will be considered; the term “wind turbine” will therefore
be used exclusively for this specific device hereinafter. In more detail, the only structural
configuration of interest will be the classic towered HAWT, with one or more blades (usually
three). Both on- and offshore turbines are included. Except for small wind turbines for
private on- or off-grid energy production, any tower size and blade length are considered.
From an engineering perspective, any wind turbine is made up of:
(i)
static, load-bearing components;
(ii)
moving/rotating parts, needed to harness the wind’s kinetic energy and turn it
into electricity.
The elements in (i) are generally referred to as the support structure. The components
of (ii) can be further divided between slowly rotating elements (blades) and high-speed
rotating mechanisms. These latter ones are all included in the rotor–nacelle assembly on
top of this support structure.
These distinctions are essential since the blades and the support structure are fields
of application for structural health monitoring, while condition monitoring, according to
the common definition of the term, deals with machinery and rapidly moving components
such as gears and bearings in the gearbox and generator.
Indeed, elements in both (i) and (ii) are subject to naturally occurring use and con-
sumption and, therefore, can develop structural damage in the long run. Damage in the
external structure will cause (partial and localized or global) collapse, while damage in the
internal mechanisms will cause faults, disruption in the energy production, and potentially
fire, explosions, oil leakage, or other events.
Hereinafter, the main structural components and pieces of machinery of a towered,
multi-bladed HAWT are reported and briefly recalled. A more detailed description can be
found in the textbook of Hau & Von Renouard [28].
Sensors 2022,22, 1627 6 of 52
3.1. Components under Structural Health Monitoring
3.1.1. The Tower
The tower is the main component of the support structure. Apart from foundation
costs (which, as it will be discussed later, vary noticeably for on- and offshore structures),
its cost can be up to one-fourth of the total (Figure 3).
The main parameter of the tower is its height. This is typically about 1.5 times the
rotor diameter; generally, it is never lower than 20 m and can reach up to 150 m or more (for
10–12 MW outputs). In absolute terms, the higher the tower, the better the wind conditions
in terms of intensity and constancy. The tower can be lattice or tubular. This second design
choice has been more common since the mid-1980s. In this case, the tower is made of
thin-walled steel conical parts of varying diameters and diameter-to-wall thickness ratios.
These offer a practical and safer way for the survey teams to access the nacelle. Moreover,
in comparison to lattice structures, there are fewer bolted joints to inspect and maintain.
The tower diameter (maximum at its basis and minimum at its top) increases with the
tower height; e.g., a typical 50 m-tall HAWT will have a diameter ranging from 3.5 m to
0.4 m [29].
From a vibrational and SHM perspective, the stiffness of the tower is the main param-
eter to be taken into consideration in evaluating the global dynamics of WTs due to the
possibility of coupled vibrations between the tower and the rotor.
Figure 3.
Estimated costs of a HAWT, as a percentage of the total and excluding foundations. Based
on data retrieved from Ref. [30].
3.1.2. The Substructure
It must be mentioned that offshore HAWTs, differently from their onshore counterparts,
include a further group of structural elements, which are included below the platform
and above the sea floor (Figure 4). These components are particularly at risk due to their
location under water or—even worse—in the splash zone, immediately above/below the
mean water level and highly subject to corrosion. Furthermore, being submerged, they
cannot easily be visually inspected if not using divers or manned/unmanned underwater
inspection robots. They are also subject to marine growth and other potentially damaging
environmental conditions such as wave, tidal, and current forces.
3.1.3. The Foundations
On- and offshore foundations differ sensibly. However, in both cases, the choice of the
specific structural design depends on the location and site conditions. For example, the
quality and strength of the soil are the main determinants affecting the size and shape of
onshore foundations, while the depth of the water and the distance from the coast are the
key factors for offshore turbines.
Sensors 2022,22, 1627 7 of 52
Figure 4. Structural components of a fixed offshore HAWT according to IEC 61400-3-1.
For onshore installations, both surface (shallow) and deep foundations are frequently
used. In the wind farms dating back to the 1990s, square foundations with a constant
thickness were commonly utilized. This solution, however, can lead to the formation of
localized damage. Hexagonal and octagonal shapes subsequently became more common,
even with variable thicknesses. The most modern designs use circular shapes, which allow
the reinforcing bars to be positioned more homogeneously. This ensures a better dispersion
of the forces induced by the soil–structure (or soil–pile–structure) interactions.
The foundation design, construction, and monitoring in offshore wind farms are more
complex and challenging. The costs are much higher as well, absorbing a large percentage
of the total expense of an offshore wind turbine. Specifically, Ref. [
31
] mentioned the
offshore foundation costs to be 35% of the total project expenses. Refs. [
31
,
32
] estimated a
cost from 352 EUR/kW (
∼
19.6% of the total construction cost) for a water depth between 10
and 20 m, up to 900 EUR/kW (
∼
35.8%) for transitional waters (40 to 50 m deep). For smaller
HAWT closer to the coast (1–2 MW, with A water depth <30 m), monopile foundations are
often encountered. Jacket/tripod substructures are more frequent between 25 and 50 m
and for larger structures (2–5 MW) [
33
]. For deep waters (50–120 m and beyond), floating
structures, anchored to the sea floor, are preferable. Conversely, gravity-based foundations
(to the right side in Figure 4) can be found for offshore installations in very shallow waters
but they are not widely used [31].
3.1.4. The Rotor
The rotor is a crucial component of the wind turbine. Indeed, it is the most expensive
mechanical component, up to circa one-fifth of the total cost [
34
]. Its design is one of the
most critical and delicate phases, especially in terms of expected performances. Therefore,
the economic feasibility of the whole system depends on these aspects.
The rotor consists of the blades and the hub from which they branch off. Generally,
the rotor can be single-, double-, or three-bladed. The most common turbines include three
blades arranged at 120
◦
from each other; this is conventionally considered the optimal design.
The length of the blades determines the capability to convert high wind speed to low
rotational speed and finally to electrical energy. As mentioned before, the diameter of
the rotor governs the tower height and thus the overall size of the structure, including
the foundations. Indeed, a larger rotor will produce a consequent increase in the energy
Sensors 2022,22, 1627 8 of 52
produced but, on the other hand, will require a wider tower cross-section and more massive
and/or deep foundations, thus increasing the construction costs.
For monitoring purposes, a “smart rotor” system should include several sensors
(accelerometers, strain gauges, pitot tubes, pressure tabs,etc.), embedded and distributed
along the blades [
35
]. Indeed, due to the prominence of the rotor blades, these components
deserve to be discussed on their own.
3.1.5. The Blades
WT blades are generally made of glass or carbon fibre reinforced polymeric (GFRP
and CFRP) materials. E-glass fibres are particularly used as the main reinforcement in the
composite material.
The blade design is actually quite complex, with several different components and
materials as pictorially described in Figure 5. This complexity makes them particularly
susceptible to manufacturing defects, which indeed were estimated in Ref. [
36
] to account
for
∼
51% of all blade damages (with debonding and voids in skin core being the most
common defects at 20% and 18%, respectively).
Figure 5.
Key components of a typical wind turbine blade. The upper and lower surfaces are also
known as the suction (or windward) and pressure (or lee) sides, respectively. The blade root bolt
connection shown here is a classic T-bolt type.
From a geometric perspective, the cross-section profile varies from root to tip, often
also rotating around its main axis; all these aerodynamical aspects of their airfoil design
characterize the blade lift-to-drag ratio and, by consequence, the wind-to-rotor efficiency.
The internal reinforcements may include different sorts of load-carrying structural elements,
such as shear webs, closed shells, box spars, or other geometries. This design choice affects
the space available for the maintenance workers to operate.
As briefly mentioned earlier, the structural integrity of WT blades is of the foremost
importance, due to the potential impacts of fully or partially detached blades with neigh-
bouring structures.
3.2. Components under Condition Monitoring
3.2.1. Drive Train (and Other Components Inside the Nacelle)
Positioned on the top of the tower, the nacelle is a cover housing, intended to shelter all
the mechanical and electrical components installed inside from the external environment.
These mechanisms include (for a conventional HAWT) the gearbox, rotor shaft, brake,
and generator, all assembled together (Figure 6, adapted from Ref. [
37
]). These pieces
Sensors 2022,22, 1627 9 of 52
are necessary for energy conversion and represent roughly between one-third and half of
the HAWT total cost on their own [
34
]. For these reasons, they will be discussed in more
detail separately.
Figure 6. Mechanical and electrical components inside the nacelle of a conventional HAWT.
Other mechanisms included in the tower and the nacelle are, for instance, the rotor
yaw system and the control and power electronics systems.
The yaw system is responsible for the orientation of the nacelle–rotor assembly towards
the wind, rotating 360
◦
around the vertical axis. The control and power electronics systems
are tools of fundamental importance to control the operation of the machine, manage the
supply of electricity, and stop the system beyond certain wind speeds for safety reasons due
to the excessive heat (generated by the friction of the rotor on the axis) and/or mechanical
stresses. Therefore, they maximize the life of the system by ensuring the limitation of
the fatigue of all components (fatigue that can result due to changes in wind speed and
direction, the presence of turbulence, shutdown, and start-up cycles of the turbine, etc.).
These systems include speed, position, temperature, and voltage sensors; mechanical
or electrical controllers; actuators; plus valves; switches; microprocessors; and many
other components.
3.2.2. The Gearbox
The main rotating machinery in a typical HAWT is the gearbox, which is also the
element most prone to mechanical faults. Therefore, it is the component of major interest
for Condition Monitoring.
Gearboxes in HAWTs are generally multi-stage, with one or more sequential planetary
stages, followed by one or more parallel stages (i.e., helical gears). The rationale is that
the speed of the rotor axis (in the order of magnitude of tens of revolutions per minute,
rpm, depending on the wind) is not sufficient for the generator to produce electricity
cost-efficiently.
In the typical drive train configuration, the gearbox acts as a rotation speed multiplier,
connecting a low-rpm, high-torque shaft on the rotor side (known as the input, slow, or
main shaft) to a high-speed (output) shaft on the generator side [
28
]. The support bearings,
mechanical brake, and rotating parts of the generator make up the rest of this common
configuration. This can increase the revolutions per minute from 12–30 up to 1200–1800.
The efficiency of the gearbox is also linked to the lubricant conditions, as will be discussed in
the following sections. These refer to both the oil cleanliness in terms of particle content and
the oil viscosity (which influences the thickness of the oil film in the gears and bearings).
Sensors 2022,22, 1627 10 of 52
3.2.3. The Generator
Located behind the gearbox and driven by the high-speed shaft, the electric generator
accommodates the mechanical and electrical components needed to convert the incom-
ing rotation into electricity. In comparison to other generators, the ones in use for wind
power need to adapt to a fluctuating mechanical power (torque) source. Furthermore, for
wind turbines directly or indirectly connected to local or national grids, synchronous or
asynchronous alternators are required to produce electrical energy at the grid frequency
(generally 50 Hz or 60 Hz, depending on the national standards). Traditionally, squirrel-
cage induction machines and synchronous machines have been used for small scale WTs;
doubly-fed induction generators are considered the dominant technology for larger mod-
els [
38
]. Other technologies include the permanent-magnet, switched reluctance, and
high-temperature superconducting generators. A comparative analysis of the benefits and
limitations of some options can be found in Ref. [39].
3.3. Incidence and Main Causes of Structural Collapse
In general terms, structural components are much less likely statistically to be subject
to damage and failure than mechanical and electrical components. This is shown in detail
in the statistics in Figure 7. For the tower, typical failure mechanisms are caused by bolt
loosening induced by dynamic loads [
40
]. However, as can be seen from Figure 7b, WT
blades are the structural elements most affected by damage. This is due to their higher
complexity, particular materials, and exposition to strong dynamic loads. The static load-
carrying elements (tower structure and substructure, plus foundations) are considered to
be less damage-prone. Among the many potential causes of their catastrophic collapse, the
structure of the metallic tower is subject to all the classic risks of thin-walled shells, e.g.,
buckling. Even concrete-made towers may suffer total collapse due to damage at the base
(at least one case was reported in Germany in 2000 [41]).
Figure 7.
Damage and failure statistics. (
a
) Percentage of unforeseen malfunctions as recorded in
Germany for 1500 wind turbines. Based on data retrieved from Ref. [
42
], collected over 15 years
(34,582 events). (
b
,
c
) percentage distribution of the total number of failures and downtime for WTs.
Based on data retrieved from Ref. [
43
], collected from several sources in Sweden, totalling about
600 WTs from 2000 to 2004 (1202 events, 156,202 h). The nomenclature used in the original sources is
reproduced for all charts.
Sensors 2022,22, 1627 11 of 52
During their operational lifespan (commonly 20–25 years), the blades are subject
to rapidly changing dynamic loads, which can cause fatigue damage [
44
,
45
], plus other
environmental conditions such as rain, humidity, and abrasive wind-carried dust or other
kinds of particulate matter. Even the simple uninterrupted exposition to sun-radiated
ultraviolet rays causes durability issues in the long term. All these eventualities might
cause surface damage and/or corrosion.
Moreover, the blades can be hit by larger foreign objects, such as hailstone and bird
strikes, and (commonly) by lightning. These two latter factors account (in the same order)
for 16% and 20% of the total damage causes according to Ref. [
36
], making them the most
common causes after manufacturing defects. It is estimated that one-third of all damages
on blades happens during storm events [46].
The blade tip and trailing edge (refer to Figure 5) are generally designed and shaped
to minimize aerodynamic noise, which makes them thin and particularly prone to the
occurrence of mechanical damage. A full-scale static test showed that a 40 m-long E-
glass/epoxy composite WT blade can bear tip deflections up to 11 m under flap-wise
loading before structural collapse [
47
]. This large flexibility, however, may cause the blade
tip to hit the HAWT tower under exceptional wind conditions. Due to its cross-section, the
blade is much stiffer edge-wise. In conclusion, seven categories of damage are considered
as the most common ones in composite blades; these are reported in Table 1.
Table 1. Typical damage typologies in WT blades, according to Refs. [48,49].
Damage Type Description
#1 Damage formation and growth in the adhesive layer joining the skin
and main spar flanges (skin/adhesive debonding and/or the main
spar/adhesive layer debonding).
#2 Damage formation and growth in the adhesive layer joining the up-
and downwind skins along leading and/or trailing edges (adhesive
joint failure between skins).
#3 Damage formation and growth at the interface between the face and
core in the sandwich panels in skins and the main spar web (sandwich
panel face/core debonding).
#4
Internal damage formation and growth in laminates in the skin and/or
main spar flanges, under a tensile or compression load (delamination
driven by a tensional or a buckling load).
#5
Splitting and fracture of separate fibres in the laminates of the skin and
main spar (fibre failure in tension; laminate failure in compression).
#6 Buckling of the skin due to damage formation and growth in the bond
between the skin and main spar under a compressive load. *
#7 Formation and growth of cracks in the gel coat; debonding of the
gel-coat from the skin (gelcoat cracking and gel-coat/skin debonding).
* Type #6 can be seen as a particular case of Type #1 damage [50].
3.4. The Incidence and Main Causes of Mechanical Failure
Recalling Figure 7b,c, one can see that the mechanical failures (including brakes, gears,
drive train, and generator) are preponderant in terms of both total failures and downtime.
For this reason, the majority of scientific research focuses on the CM of these components
housed inside the nacelle.
In the context of mechanical components, the concept of the “failure rate” is generally
applied. The standard time-scheduled maintenance, therefore, should follow the classic
bathtub curve, focusing on early-stage monitoring (to assess “infant mortality”, due to
defective components) and in the long run, where wear out failures start to become predom-
inant. During its life cycle, any wind turbine is however subject to the random occurrence
of unexpected faults; this represents a statistical constant along time. In general, gearbox
Sensors 2022,22, 1627 12 of 52
failures happen slightly more frequently due to late wear out failures compared to early
infant mortality [
21
]. A study over a 13-year timeframe showed a failure rate from 0.10 to
0.15 mechanical failure/turbine/year for land-based European WTs [51].
Bearings are, arguably, the most critical component of the gearbox. A 2013 report
from the U.S. National Renewable Energy Laboratory (NREL) [
52
] estimated that about
70% of all gearbox failures are due to bearing failures. More specifically, the bearing life of
the parallel stages is generally considered to be very high, in many cases over 1 million
hours [
20
], even if bearing failures at this stage are nevertheless not uncommon (see, e.g.,
Ref. [
53
]). On the other hand, bearings in the planetary stages often do not fulfil their
design lifespan [
20
]. This has also been assessed in many experimental campaigns [
53
] and
it is quite understandable due to the large torque applied at the planetary gear stages. The
reliability and durability of these bearings have always been considered one of the major
issues for WTs [
54
]. Many failures are observed in the bearing inner rings of the planetary
stages since these are exposed to ∼5 times more high load cycles than the outer rings [20].
Table 2reports a brief description of the most common bearing failure typologies, according
to the nomenclature reported in Ref. [55].
Table 2. Typical damage typologies in bearings, according to Ref. [55].
Damage Type Description Possible causes
Flaking Creation of regions with a rough and
coarse texture due to the splitting off of
small pieces from the raceway surface.
Rolling fatigue, caused in turn by excessive load,
misalignment, poor lubrification, water or debris
inclusions, unsuitable bearing clearance, unevenness in
housing rigidity, rust, corrosion pits, dents.
Peeling Light wear and dull spots on the surface,
with micrometric cracks and
minor flaking.
Poor or unsuitable lubricant, debris intrusion in
the lubricant.
Scoring Straight lines on the surface,
circumferentially on the raceway surface.
Generated by accumulated small seizures, caused in
turn by sliding under improper lubrication or
excessive/improper loads and conditions (shaft
bending, the inclination of inner and outer rings, etc).
Smearing Surface damage, with the formation of
rough and partially melted material.
Generated by accumulated small seizures between
bearing components, caused in turn by oil film rupture
(because of poor/improper lubrication or high speeds
with very light loads).
Fracture
Small pieces broke off due to shock loads
or stress accumulation. Impacts during mounting/dismounting, excessive
loads, progression of surface cracks.
Cracks Formation of surface cracks on the
raceway rings and/or rolling elements.
Excessive loads, progression of flaking damage,
creep-induced heating, inappropriate shaft (e.g., poor
taper angle).
Cage damage Cage deformation, fracture, and/or wear
(considering the cage guide surface,
pocket surface, and cage pillars).
Excessive speed, sudden acceleration/deceleration, high
temperature, poor lubrication, excessive vibrations,
bearing misalignment.
Denting Small dents on the surface of raceway
rings or rolling elements. Caused by metallic particles or other very small debris
caught in the surface during rolling.
Pitting Pitted surface on the raceway rings or
rolling elements. Poor lubricant, debris in the lubricant, or exposure
to moisture.
Wear Surface deterioration on the raceway
rings, rolling elements, cage pockets,
and/or roller end faces.
Sliding friction between two surfaces, caused in turn by
an irregular motion of the rolling elements, poor
lubrication, debris intrusions in the lubricant, or as a
progression from chemical or electrical corrosion.
Sensors 2022,22, 1627 13 of 52
Table 2. Cont.
Damage Type Description Possible causes
Fretting
Corrosion happening at the contact area
between the raceway ring and the rolling
elements. It may happen at regular roller
pitch intervals.
Repeated sliding on the fitting surface.
False brinelling
Hollow spots that resemble Brinell dents.
Caused by wear, induced in turn by vibration and
swaying at the contact points between the raceway and
the rolling elements, especially with poor lubrication.
Creep Shiny appearance on the fitting surface,
potentially coupled with scoring
and wear.
Relative slipping at the fitting surfaces, due to a loose fit
or insufficient sleeve tightening.
Seizure Softened, deformed, and/or melt
material in the raceway rings, rolling
elements, or cage.
Excessive load, speed, shaft bending, poor housing or
lubrication, small internal clearance.
Electrical corrosion Corrugations resulting from locally
melted material.
Melting by arcing, induced by the passage of electric
currents. In turn, these are induced by the electrical
potential between the inner and the outer rings.
Pit corrosion Pits on the surface of raceway rings or
rolling elements due to
chemical corrosion.
Entry of corrosive gas or liquid, improper lubricant,
moisture, high humidity, improper handling and
storage conditions.
Mounting flaws
Scratches on the surface of raceway rings
or rolling elements caused by
mounting/dismounting.
Incorrect mounting/dismounting (impulse loads, the
inclination of inner or outer rings, etc).
Discolouration Discolouration of the cage, rolling
elements, or raceway rings. Poor lubrication and/or high temperature.
On the other hand, the gearbox gears can be generally assumed to fulfil their design
life as well, even if their reliability is often lower than expected and depends on the
manufacturer’s design, tooth profile, and material quality [
20
]. Surface fatigue cracks and
tooth bending fatigue are among the most common causes [20].
Outside of the gearbox, the main shaft bearings are considered to be much less critical
and are generally capable of fulfilling their design life [
56
]. The generator presents some
rotating components that require CM as well. This can be performed with vibration-
based inspection (VBI) and signal processing approaches (two examples are reported in
Refs. [57,58]) or using alternative methods such as temperature trend analysis [59].
In conclusion, Table 3reports the main causes of mechanical failure according to the
scientific literature. However, one should remember that, apart from purely mechanical
failures, generators are (obviously) at risk of the failure of electrical materials [60].
Table 3. Typical failure modes in wind turbine mechanical components, according to Ref. [61].
Mechanical Component Common Failure Modes
Gearbox and drive train
Gear tooth damages, high- or low-speed shafts faults, gearbox bearing failures.
Generator Generator stator failure, generator rotor failure, generator bearing failure.
Main bearing Bearing failure, bearing rubs, bearing looseness
Pitch gears Pitch Gear tooth damages.
Yaw gears Yaw Gear tooth damages.
3.5. Survey and Maintenance Policy for Offshore Wind Farms
Due to their greater logistic complexity and higher O&M costs, a brief discussion
about the maintenance strategies for on- and offshore wind farms is needed.
Sensors 2022,22, 1627 14 of 52
“Corrective” or “reactive” maintenance is, by definition, performed after failure detec-
tion, aiming at the restoration of an asset to a condition in which it can perform its intended
function. It is both unscheduled and unplanned. Thus, corrective maintenance is often
unavoidable, with the maintenance teams having to respond to equipment breakdown
or failure immediately with little to no pre-alarm and often without a totally clear under-
standing of the exact cause of damage/failure. “Preventive” maintenance, on the contrary,
aims to carry out an overhaul, replacement, or repair before the component fails, in a
pre-planned, predictable fashion. This can help to reduce downtimes while also improving
efficient resource planning.
Preventive maintenance can be further divided into pre-determined (time-scheduled)
and predictive (condition-based) maintenance. This scheme is the one currently set by the
current European standard, as dictated by the norm EN 13306:2017 (depicted in Figure 8).
Figure 8. Maintenance strategies according to EN 13306:2017.
The current trend in the wind industry is to transition from time-scheduled to condition-
based monitoring, using embedded sensors and continuous, permanent monitoring sys-
tems to detect early signs of anomalies at a global level. This is mostly achieved through
vibration-based signal processing, SCADA data analysis, and statistical pattern recognition,
applying artificial intelligence strategies (mainly machine learning and artificial neural
networks). As mentioned, these aspects will be all addressed in future, dedicated works.
However, once an anomaly is detected (or even localized) at a global level, on-site main-
tenance is still required for in-depth, localized analysis. Therefore, local NDE and global
vibrational approaches are not mutually exclusive. On the contrary, they both allow fast
and cost-efficient maintenance planning. This concept is a key component of the “Intelligent
Maintenance” framework, based on a globally optimum trade-off between prevention and
repair costs [37].
As already mentioned before, the issue of proper maintenance planning is even more
prominent for offshore installations. Table 4reports the main factors that affect the selection
of the maintenance and survey strategy for offshore wind farms, according to selected
authors from the recent scientific literature.
Table 4. Main factors influencing the choice of the maintenance strategy for offshore wind farms.
Study Year Mentioned Factors
Henderson et al. [62] 2003 Accessibility of the offshore platform and reliability of the
monitoring strategy.
Nielsen et al. [63] 2011 Weather conditions, total power generation, repair strategies,
transportation strategies.
Dinwoodie et al. [64] 2012 Repair time, wave height, wind speed, number of wind turbines in the
wind farm, ship availability, availability of spare parts stocks.
Scheu et al. [65] 2012 Expected typologies of component failures, ship fleet size, ship type,
travel time, number of maintenance workers on staff.
Sensors 2022,22, 1627 15 of 52
Table 4. Cont.
Study Year Mentioned Factors
Besnard et al. [66] 2013
Location of accommodation facilities for maintenance staff, vessels for
the transfer of crew (type and number), availability of helicopters,
organization of work shifts, management of spare parts stocks, technical
support, availability of cranes (purchase or contract), environmental
conditions (depending on weather and season), economic parameters
(electricity prices, ship rental costs).
Halvorsen-Weare et al. [67] 2013
Investment costs, ship costs (fixed and variable costs), failure probability,
downtime costs, meteorological data.
Hofmann & Sperstad [68] 2013 Weather conditions (including uncertainty), breakdown rates, electricity
price, ship price (costs, fleet composition, type, quantity), workers (shift
length, quantity), location of the maintenance base of operations.
Perveen et al. [69] 2014 Protection methodologies, occurrence of cable and component failures,
repair strategy, wind speed predictions, and condition
monitoring systems.
Endrerud et al. [70] 2015 Weather conditions, ships (availability, operating limits, costs),
availability of maintenance technicians, repair times, wind farm layout,
cost of spare parts, logistics (warehousing and other costs).
Nguyen & Chou [71] 2018 Duration of maintenance (downtime), expected loss of production
during maintenance time, the market price of electricity, location of the
wind farm.
4. Main Applications for NDE Techniques in Wind Turbines
Non-destructive evaluation strategies and techniques can be applied for SHM, CM, or
both. They are used to evaluate the (global or local) structural integrity of the load-bearing
and mechanical components, as well as to detect growing cracks, manufacturing defects,
or the deleterious consequences of external actions or prolonged operating conditions.
However, some specificities depend on the particular system under investigation. For
instance, the SHM of foundations (especially deep ones) has its own specific needs and
more compelling limitations. WT blades, for economic reasons, are generally made of
fibre-reinforced plastic. However, these composite materials are quite prone to develop
manufacturing defects and are more difficult to assess in detail than other more conven-
tional building materials (e.g., structural steel). Finally, the drive train and other mechanical
components inside the nacelle are subject to a high torque and/or speed rotation and/or
temperatures, differently from the wind turbine external structure.
For these reasons, it is possible to define some well-defined fields of application,
described hereinafter.
4.1. Condition Monitoring of the Mechanical Components
Arguably, this is the most relevant aspect due to the statistical prominence of mechani-
cal faults over structural collapses in WTs, as discussed before. Indeed, it can be considered
as the main focus for the whole wind industry [
72
]. Vibration-based approaches are one
of the main strategies in this ambit, even codified by specific requirements (see e.g., ISO
13373-1:2002 and ISO 61400-25-6:2016). SCADA data analysis is also highly estimated for
this aim. However, as mentioned earlier, due to the vastness of this specific argument, these
approaches are not included here and will be deferred to a dedicated future work. Oil
monitoring is another very common approach, while other NDTs, often performed locally,
include acoustic emissions, infrared thermography, and electromagnetic testing. These will
be all discussed in detail in the next sections.
Sensors 2022,22, 1627 16 of 52
4.2. SHM of the Wind Turbine Blades (Blade Monitoring)
Probably the second most important aspect, again due to the relative frequency of
(partial or total) blade failures. To account for all the potential sources of damage enlisted
in the previous section, acoustic emissions and strain measurements are commonly applied,
both on- or off-site, at different life stages of the blade (quality checks after manufacturing,
routine maintenance, extraordinary repairs, and forensic studies on collapsed specimens).
Many studies involve the dynamic and/or static characterisation of the WT blades, off-
site or in situ. Of course, the first case generally produces more reliable results, yet it requires
detaching the blades from the rotor and carrying them in a laboratory, with all the related
costs and delays. On-site inspection, on the other hand, can be performed (depending on
the technology) on stationary blades, temporarily halting the energy production, or during
operations, without interruptions.
The National Wind Technology Center (NWTC) of the NREL performed a remarkable
full-scale fatigue testing of two 9 m-long CX-100 WT blades (one pristine benchmark and
one with purposely inserted defects) [
73
,
74
]. Strain gauges, piezoelectric transducers, and
accelerometers were used to measure the vibrational response of the two systems. These
were analyzed to identify the onset of fatigue injury and to study its progression with a
variety of different signal processing techniques.
Generally, many different sensing techniques are jointly applied for a complete as-
sessment; e.g., the European project ReNEWiT developed a lightweight, automated multi-
sensor gantry system for the monitoring of stationary WTs (up to 40 m-long) GFRP blades.
It included an X-ray Compton backscattering system, dual laser shearography, pulsed
thermography, and phased array ultrasonic testing. These (and other) approaches will be
presented separately in the rest of this discussion.
Regarding the sensor placement, they can be located close to the blade “hot spots”,
i.e., the locations where damage is most likely to occur. These were enlisted in Ref. [
50
] as:
1. at the blade root (where the mechanical stress is maximized);
2. between 30% and 35% of the chord length;
3. at 70% of the same;
4. at the maximum chord section (subject to potential buckling);
5.
on the upper flange of the spar, at different chord lengths depending on the pitch
angle (thus on the current wind speed).
4.3. SHM of the Supporting Structure and Substructure
While overall a rare occurrence, the structural failure of the tower itself has a high level
of risk due to the severity of the potential consequences (total asset loss). In this sense, both
global and local monitoring are required. The first case deals with the overall structural
integrity and stability; this is performed, generally, with vibration or strain measurements,
even if Global Position Systems (GPSs) sensors and inclinometers are commonly used as
well to monitor potential risks of subsidence or capsizing, especially offshore. Periodic
controls to avoid tower misalignment are required by many regulators as well.
Local monitoring focuses on specific high-risk components. These damage-prone hot
spots include:
1.
welted, grouted, and bolted joints, due to their relative fragility, in particular to
fatigue damage; e.g., on tripod offshore structures, the upper central joint is the most
critical location;
2.
location exposed to an aggressive environment (e.g., underwater or, even worse,
in the splash zone). Corrosion monitoring is particularly requested in these most
endangered locations.
Thus, while much less developed than drive train CM, wind turbine SHM is gradually
gaining industrial applications as well. For instance, the German BSH standards (in force
since 2007 [
75
]) mandate that at least 1 out of every 10 offshore WTs is equipped with an
SHM apparatus dedicated to the support structure.
Sensors 2022,22, 1627 17 of 52
4.4. SHM of the Foundations
The concepts expressed for the load-bearing structure can be extended to the founda-
tions, with the additional risk induced by a higher degree of uncertainty. This derives from
both the complexity of soil–structure interactions (especially underwater on the sea bed)
and the difficulty to access for periodic checks. Indeed, deep foundations are often needed
even onshore, yet they are implicitly inaccessible. They can be assessed via above-ground
Remote Sensing (RS) techniques, at the cost of lower reliability, or through lengthy exca-
vation campaigns (when possible), at the cost of suspending wind power generation [
76
].
Both choices are sub-optimal from a cost-benefit point of view.
For offshore installations, in addition to this problem, scour monitoring is strictly
required. In particular, for monopile HAWTs, a scour-induced reduction in the foundation
integrity over time may lower the first natural frequency of the support structure, making
it dangerously closer to the frequencies where most of the broadband wave and gust
energy is contained [
77
]. Thus, more wave energy may become resonant with the structure,
increasing material fatigue [
78
]. This is even more dangerous in seismic areas due to the
potential joint action of seismic and wave loads on monopile structures [79].
Inclinometers, strain gauges, and optical fibre sensors are all commonly used for
foundation monitoring. To protect them from the harsh external environment, they can
be embedded in the concrete pour (as tested e.g., in Ref. [
80
] with fibre Bragg gratings).
The same sensors can be used to detect surface cracks in shallow foundations as well [
76
].
Displacement sensors, such as laser or infrared telemeters and tachymeters [
81
], can be
located in the foundation to monitor vertical movements considered as precursors of certain
failure mechanisms [82].
5. Non-Destructive Techniques (NDTs)
In this section, the main NDTs used for HAWT integrity monitoring are reviewed
and discussed. Please consider that, while all techniques are reported here separately for
better comprehension, they can (and should) be applied synergistically to compensate one
another for their limitations. As an example, He et al. [
83
] proposed combined ultrasound
and vibrothermography testing for the damage detection of very small surface damages in
composite panels.
For all the tables reported in this and the following section, the following inclusion
criteria have been applied. Due to the large range of different aspects, as well as the
vastness of the scientific literature on each subtopic, only a selection of relevant, recent, and
well-recognized scientific articles is included.
In more detail, all articles (except wherever specified differently) have been published
in the 2000–2021 period and had at least
≥
30 mentions as of 1 December 2021. Please
consider that the review should not be considered exhaustive, and it is therefore only
indicative of the most prominent and recent advancements in each field of research.
For each reported study, its main declared field of application will be indicated as SHM
(external structure, blades, foundations) or CM (any high-speed rotating
machinery component).
5.1. Traditional, Enhanced, and Automatic Visual Inspection (VI)
Simple Visual Inspection is still regarded as the most common form of maintenance
survey for both the tower, the exterior of the nacelle, and (especially) the WT blades. This is
generally performed from the tower bottom through binoculars or, during blade cleaning or
inspection routines, by roped maintenance staff and/or from a gantry or lift system. These
latter solutions expose the personnel to a certain level of danger and are not feasible during
bad weather conditions. Due to the limited space, the VI inspection of the mechanical
components inside the nacelle is more inconvenient, yet still feasible (visual walkarounds).
More detailed surveys can be performed thanks to borescope inspection or through the
gearboxes’ inspection pots.
Sensors 2022,22, 1627 18 of 52
The main advantage of VI is that, as a non-contact technique, it is implicitly non-
invasive, and thus does not alter the structural conditions of the monitored system (even
if a closely-related technique, dye penetrant inspection, applies some external liquid to
enhance the visual contrast of surface cracks). On the other hand, it is obviously limited to
surface damages and defects alone. Due to the scale of HAWTs, it is rather time-consuming.
Even more importantly, it is a qualitative approach; the accuracy of the assessment results
varies highly depending on the inspectors’ skill and is hampered by human errors. These
assessments are neither easily comparable between different maintenance teams.
It is possible to classify the VI strategies according to two main factors:
I if they require human personnel on-site or not;
II if they add any kind of support to the human eyesight.
The classic visual inspection is generally performed periodically, on-site, by one or
more technicians without any form of AI support or advanced device.
In case I, it is also possible to have maintenance staff members using manned platforms
(e.g., small submarines, helicopters, etc.). Otherwise, it is possible to replace the human
personnel with (autonomous or not) robot platforms that can fly, swim, crawl, hike, etc.,
to the needed location. These are essential e.g., in narrow tubes and ducts, or very cost-
efficient (when adjusted for risk) for the inspection of components located underwater or
at dangerous heights. Several examples of potential designs for a climbing robot can be
found in Ref. [
84
]. Finally, it is possible to deploy a permanent system of closed-circuit
cameras for a constant VI (as proposed e.g., with a computer-controlled pan/tilt zoom
camera system in Ref. [85]).
Case II is linked to the use or not of computer vision approaches. Generally, survey
teams are commonly equipped with hand-held devices (e.g., digital cameras). However,
these are often intended for visual documentation rather than quantitative data analysis.
These and other optical instruments can both enhance human eyesight or replace it com-
pletely. Two examples of the first case are the use of the line or edge detection methods
such as the ones applied for WT blade surface crack detection in, respectively, Ref. [
86
] and
Ref. [
87
]. In this sense, the Canny algorithm was found to be the most efficient choice in
many similar studies [
88
,
89
]. Similar techniques were utilized to estimate the extension of
the surface area damaged by machining processes (e.g., drilling) in composite laminates for
WT blades [90].
In this second case, the term automated visual inspection is used. Automatic computer
vision approaches can be image- or video-based. These allow for the inspection of large
surfaces rapidly and reliably, even for the smallest surface defects, including e.g., barely
visible impact damages (BVIDs) due to bird strikes on composite materials.
To summarize, non-conventional VI strategies can be classified (as in Table 5) de-
pending on the (manned or unmanned) platform and the potential use of any Computer
Vision techniques.
Table 5.
Some notable and recent examples of advanced and automated VI strategies applied for the
NDE of wind turbines.
Study Year Platform
Computer Vision/Video or
Image Processing1
Technique
Application
Stokkeland et al. [91] 2015 Digital camera-equipped
multi-copter UAV.
Computer Vision was also utilized for
autonomous navigation (moving along the
blades to acquire pictures). SHM
Park et al. [92] 2015 Fixed Digital camera
(laboratory test only)
Image segmentation, canny edge detection,
and Hough Transform are applied to
evaluate the angle changes in the nuts. The
method is proposed for bolt loosening
monitoring in the ring flange joints in
WT towers.
SHM
Sensors 2022,22, 1627 19 of 52
Table 5. Cont.
Study Year Platform
Computer Vision/Video or
Image Processing1
Technique
Application
Wang et al. [93] 2017 Remotely-controlled,
digital
camera-equipped UAV.
Cascading classifiers (several variants)
were applied to detect and locate pixel
regions containing cracks in the images. SHM
Reddy et al. [94] 2019 Digital camera-equipped
multi-copter UAV. A convolutional neural network
(CNN) architecture. SHM
Shihavuddin et al. 1[95]. 2019 Digital camera-equipped
multi-copter UAV.
Deep learning-based damage detection and
classification. Specifically, the authors used
the well-established faster region-based
CNN (R-CNN) algorithm [96] and
compared it to other similar architectures
(R-CNN, Fast R-CNN, SSD, and R-FCN).
SHM
Yang et al. [97] 2021 Digital
camera-equipped UAV.
The authors used the pre-trained CNNs
described in Ref. [98] after integrating
them with their image dataset via
Transfer Learning 2.
SHM
1
A dataset of several hundred UAV-taken pictures of a single wind turbine has been released linked to this
study [
99
].
2
Few pictures of wind turbine blades are available easily within common annotated image datasets
such as ImageNet and AlexNet.
Regarding the use of robotized inspection, unmanned aerial vehicles (UAVs) are
the predominant choice. Other non-flying alternatives are more uncommon and have
received less attention from practitioners and researchers alike. For instance, Lim et al. [
100
]
proposed the concept of an inchworm-like robot, with telescopic motion, for blade VI. The
concept is intended to carry several sensors, including a camera. Damage detection and
localisation are achieved for surface cracks using computer vision and blob labelling.
Regarding computer vision and automatic VI, the most common approach is to use
deep neural networks (DNNs), especially some sort of region-based convolutional neural
networks such as the Fast R-CNN [
101
], faster R-CNN [
96
], or similar variants, to au-
tomatically detect, localize, and estimate the severity of surface cracks. Often, reliable
R-CNNs architectures, pre-trained over some generic image dataset, are employed after
transfer learning, fine-tuning, and/or retraining with specific datasets, many of which are
already available online. Depending on the typologies of damage included in the training
dataset, AI can assess different damages such as cracks, breakages, oil stains, etc. [
102
] Some
recently-developed and fast-growing pre-trained models include VGG-16 [
103
], ResNet-
v2 [
104
], Inceptionv4 [
105
], YOLOv5 [
106
], and EfficientNet [
107
]. Several variants of these
models are available, including hybrid architectures such as e.g., Inception-ResNet-v2 [
105
].
One recent example can be found in Ref. [108].
However, VI is not the only non-contact approach for NDT. Other RS techniques
include, for instance, optical measurement techniques, laser-based approaches, and video
spectroscopy.
5.2. Optical Methods
Image and video processing are wide research fields. Some optical measurement
technologies are well-established and extensively used; the classic example is digital image
correlation (DIC), which is well-established for the experimental testing of materials in
general [
109
] and nowadays is quite common for the full-field strain inspection of WT
blades in particular (see e.g., Refs. [
110
,
111
]; other highly cited works are reported in
Table 6). If multiple cameras are available, stereophotogrammetry can be used for strain
measurements as well [
112
]. The method has been tested as well for drone-borne images
taken by a multi-copter UAV [113].
Apart from blade monitoring, DIC measurements have been (less commonly) applied
to the tower structure as well (e.g., in Ref. [
114
]). Instead, no relevant applications for CM
were found during this literature review.
Sensors 2022,22, 1627 20 of 52
A more recent video processing technique, the phase-based motion magnification
(PBMM [
115
]), is also gaining researchers’ attention as a means to extract modal parameters
from imperceptible vibrations [
116
,
117
]. This technique has been proven to be feasible
for SHM purposes [
88
,
89
] and applied in combination with stereophotogrammetry to WT
blades [
118
]. All these techniques resort, in some ways, to the pixels’ brightness (amplitude)
and/or phase and are therefore generally limited to the visible band of the electromagnetic
spectrum. They can be preferably used off-site under controlled laboratory conditions
(illumination etc.) but have been validated for outdoor investigation on-site as well, with
natural illumination.
Table 6.
Some notable and recent examples of DIC and other optical techniques applied for the NDE
of wind turbines.
Study Year Technique Notes Application
Baqersad et al. [119] 2012 3D DIC
The authors used two stereoscopic
high-speed cameras to record the
vibrations of a WT blade with
optical targets attached to its
surface (excited with
hammer hits).
SHM
LeBlanc et al. [120] 2013 3D DIC
The full-field displacement and
strain fields of one CX-100
9 m-long WT blade were
estimated. The damaged areas
were located from discontinuities
in the curvature shapes.
SHM
Winstroth et al. [121] 2014 3D DIC and
point tracking
A random black-and-white dot
pattern was applied at four
different radial positions on one
blade of a three-bladed rotor. The
tests were performed in situ on the
operating HAWT.
SHM
Carr et al. [122] 2016 DIC and 3D Dynamic
Point Tracking (3DPT)
The authors compared the
dynamic stress and strain fields
obtained with their
video-extracted measurements
with the readings from attached
strain gauges.
SHM
Another optical method is optical coherence tomography (OCT), firstly proposed for
the noninvasive cross-sectional imaging of biological tissues [
123
] and then further refined
for high depth resolution in Ref. [
124
]. The method is based on an external broad bandwidth
light source, a charge-coupled device camera, and a series of other components (the setup
change accordingly to the specific implementation, several variants exist). The technique
has been proven to be suitable for the ultrahigh-resolution imaging of internal defects in
fibre-reinforced polymers such as the ones currently used for WT blade manufacturing
(CFRP and GFRP) [
125
]. For these materials, the method was compared to X-ray scanning
in [
126
]. Very recently, Ref. [
127
] used near- and mid-infrared ultrahigh-resolution OCT
for subsurface defects on metal samples covered with marine coatings. This application is
well-suited for the undersea parts of the substructure of an offshore HAWT.
One highly cited work (>30 citations as of 1 December 2021) was found in the scientific
literature for OCT; Liu et al. [
128
] studied the delamination growth in a GFRP WT blade. A
tridimensional geometric model of the crack surfaces was reconstructed.
Sensors 2022,22, 1627 21 of 52
5.3. Laser-Based Measurement Techniques (LDV, LiDAR, and Shearography)
Some applications of laser technology in HAWT monitoring include laser vibrometry
and laser scanning [
129
]. However, apart from the specific acquisition techniques, 1D,
2D, or 3D laser Doppler velocimeters (LDVs) will return velocity time series that can be
used for vibration-based SHM, which will be discussed elsewhere. Another example of
laser-based approaches is the light detection and ranging (LiDAR) technology, which was
proposed e.g., in Schäfer et al. [
130
] to be mounted on a multicopter UAV prototype for the
3D mapping of HAWTs in situ.
Finally, shearography testing (ST), is another optical technique that applies coherent
laser illumination for surface deformation measurements. It can be considered as a sort
of speckle interferometry with laser point patterns; thus, it is a short-range laser-based
NDT. ST offers full-field and fast (even real-time) non-contact imaging; it can then be
used for defect and damage detection by searching for deformation anomalies [
131
]. The
technique has been investigated since the early 1990s [
132
], yet is used mainly for indoor
industrial applications or under controlled laboratory conditions only (e.g., Ref. [
133
]), even
if portable devices for in situ testing are nowadays available. However, this equipment is
generally more expensive and complex than the ones for other NDTs [133].
Shearography has been applied to detect delamination, debonding, impact damage,
wrinkles, and dry spots in composite WT blades [
131
]; to monitor their response under
quasi-static failure tests [
132
]; to evaluate the soundness of bonding in laminated compos-
ites [
134
]; and to assess bird strike BVID in carbon/glass fibre reinforced plastic sandwich
panels [
135
]. Its results have been compared to the ones from active infrared thermography
(in Refs. [
136
,
137
]) and the ones from image correlation, acoustic emission, fibre-optic
strain sensing, and piezoelectric sensing (in Ref. [
138
]). From these and other studies, it
has emerged that the ST technology may still require future research [
139
]. Nevertheless,
some related studies have been published very recently (e.g, Ref. [
140
]), even including
remotely-controlled robotic platforms [141].
5.4. Video Spectroscopy
Even not considering laser-based approaches, optical methods are not strictly limited
to the visible portion of the electromagnetic spectrum (i.e., wavelengths of 380–700 nm, see
Figure 9). The Near-Infrared (NIR, ~700–~900 nm), as well as the Short-, Medium-, and
Long-Wavelength infrared ranges (SWIR, ~900–~1700 nm, MWIR, ~3000–~5000 nm, and
LWIR, ~8000–~14000 nm, respectively) have all been successfully applied for RS in several
engineering applications. Furthermore, the combined use of visible and NIR-SWIR-MWIR
can be easily achieved by combining standard cameras with multi- or hyper-spectral sensors
and thermographic cameras. For instance, the sensor payload proposed in Ref. [
142
] for a
low altitude land survey was intended to allow for synchronous acquisitions over a band
of more than 10
4
nm, with overlaps between the different sensors. This presents several
practical advantages since visible, NIR, and SWIR electromagnetic emissions are mainly
reflected radiations, while MWIR and LWIR are mostly emitted from the object itself. The
former group can provide information regardingthe chemical composition of the irradiated
object, according to its absorbed bands, while the latter depend instead on its thermal state
and energy content. For these reasons, thermographic cameras will be discussed on their
own in a dedicated subsection. Short and very short wavelengths (X-rays and gamma rays)
will be treated separately as well.
Regarding multi- and hyper-spectral imaging, the difference between the two terms
lies solely in the resolution in terms of wavelength bands. Multispectral imaging considers
a small number (typically 3 to 15) of spectral bands, while commercially available hyper-
spectral sensors can discern 244 bands in the VNIR range (sampling 2.3 nm per band) and
254 in the SWIR one (5.8 nm per band) [
143
]. In both cases, the spectral signature of each
material can be used for supervised or unsupervised classification; many algorithms have
been developed for this aim.
Sensors 2022,22, 1627 22 of 52
Figure 9.
The range of the electromagnetic spectrum that can be covered by common optical tech-
niques (digital, multi/hyperspectral, and thermographic cameras), as well as Gamma-ray, X-ray,
microwave, and terahertz testing technologies.
This research field is, however, still underdeveloped for WT applications.
Rizk et al. [144]
discussed the potential offered by a hyperspectral imaging system in detecting damage
to WT blades and icing events. Several damage typologies were considered; the results
demonstrated that hyperspectral imaging could detect surface and subsurface defects, as
well as icing events in their early onset stages. A similar approach was then further tested
for blade defect detection [145].
Finally, considering even longer wavelengths (>1 mm), microwave sensors have been
validated for remote sensing testing, as will be discussed later in a dedicated section.
5.5. Infrared Thermography (IRT) and Other Temperature Measurements
Infrared thermography, also known as thermal imaging, is one of the most common
optical techniques. Indeed, it is applied extensively both for SHM purposes to the external
structure and blades and for the CM of the rotating machinery inside the nacelle.
The IR sensors can be single-point transmitters or cameras with different levels of
spatial definition (generally in the order of the hundreds or thousands of pixels). Many
commercial cameras offer a dual view (visible and IR) simultaneous recording, both with
a pan/tilt control or fixed angle of view. They can also be deployed in parallel and
directly connected to a SCADA system. They do not need to be in direct contact with the
target; this is especially useful for hot machines. However, normally, the sensor must be
mounted to a close distance from the object to obtain reliable readings. Because of this
requirement, it is very rare to temperature monitor more than one target with a single IR
transmitter/camera. Due to the implicit line-of-sight limitations, IRT is generally limited
to a single surface; however, 3D scanning and computer-aided model reconstruction also
allow for 3D thermography over all the external surfaces of a single object. This was proven
to be feasible for WT blades in Ref. [146].
5.5.1. Passive IRT
Inside the nacelle (indoor), temperature readings are particularly useful for the CM of
the gearbox, mechanical brake, generator, main shaft bearing, the yaw and pitch systems,
and the pump motor of the hydraulic system. In Ref. [
147
], it was suggested that any
temperature rise between +1 and +10
◦
C should be considered as a potential index of
minor damage. The same guidelines suggest planning for a repair in 2 to 4 weeks for an
increase between +10 and +35
◦
C, in 1–2 days for +35–+75
◦
C, and immediately if higher
than +75
◦
C (all these indications must be corrected according to the environmental and
Sensors 2022,22, 1627 23 of 52
operating conditions). Thermal images can be used for supervised learning, training a
classifier with readings from the healthy conditions as completed e.g., for brushless DC
motors in Ref. [
148
]. Furthermore, these can be used for the detection of non-mechanical
failures such as fire detection and the monitoring of the high voltage transformer and other
electrical systems (power electronics, control system, etc.). All these uses are well-described
in Refs. [149,150].
Apart from damage detection, IRT is especially well-established outdoor for ice de-
tection on WT blades (see e.g., Ref. [151]). This is relevant from an SHM perspective since
freezing conditions are known to increase the stiffness of the structure [152] and therefore
can change the vibrational response of the same. In turn, this might cause false alarms for
vibration-based anomaly detection, if these confounding influences (i.e., damage-unrelated
environmental effects) are not actively depurated from the recorded output. For temper-
ature monitoring under operating conditions, Ref. [
153
] recently proposed a line laser
thermography approach, reading a fixed point along the rotating blade. IRT can even
be used to monitor the blade de-icing systems (generally an embedded heating wire) for
electrical breakdowns [154].
5.5.2. Active IRT
Indoor and outdoor IRT are not limited to passive thermography. Active heating and
cooling thermography techniques are available for different purposes; a large set of options
were investigated on WT blade samples in Ref. [
155
]. However, these approaches need
some sort of controllable thermal excitation. This can be induced through, e.g., thermal
emitters, ultrasounds, microwaves, eddy currents, flash lamps, etc.
In the case of surface heating (flash lamps [
156
], lasers [
157
], etc.), the properties of
surface defects (such as their depth [
158
]) can be estimated based on the heat conduction
from the surface inward. This approach is known as surface heating thermography (SHT).
It has been also referred to as optical thermography [
159
] and it is (generally) performed
in reflection mode, i.e., with the IR camera and the source heat on the same side of the
target surface. Pulsed, pulsed phase, stepped, modulated, lock-in, and line scanning
thermography are all feasible within SHT.
On the other hand, microwaves, ultrasounds, and high-frequency induction currents
can be used for volume heating thermography (VHT), i.e., heating inside out. These
applications are also known as non-optical thermography. A more detailed discussion
about optical and non-optical excitation sources can be found in Ref. [160].
According to the energy source, it is possible to further classify the VHT techniques as:
1.
eddy current (EC) thermography (or inductive thermography), based on the heath re-
leased by resistive losses to the eddy currents induced by electromagnetic pulses [
161
];
2.
microwave thermography, based on the well-known principles of microwave heating.
The electromagnetic energy is absorbed volumetrically by the target object, favouring
uniform and rapid self-heating;
3. vibrothermography (or thermo-sonic testing), with mechanical waves.
Note that, due to their low electrical conductivity and magnetic permeability, fibre-
reinforced polymers have a high penetration depth (about 50 mm for CFRP under 100 kHz
excitation) and thus can be volumetrically heated with ECs [162].
Some VHT approaches include EC pulsed [
163
], pulsed phase [
162
,
164
], stepped [
165
],
and lock-in [
166
] thermography, as well as microwave pulsed and lock-in thermogra-
phy [
167
]. Most of these strategies can be performed with ultrasonic waves too; another less
widespread alternative is the ultrasound-burst-phase thermography [
168
]. The links be-
tween these VHT techniques and their SHT counterparts, mentioned before, are graphically
displayed in Figure 10.
Sensors 2022,22, 1627 24 of 52
Figure 10. The main IRT techniques available as of 2021.
For completeness’ sake, the main aspects of the most prominent active IRT techniques
will be recalled in the next paragraph, considering both surface and volume heat sources.
As the name suggests, pulsed thermography is based on short-duration energy pulses,
while the pulsed phase is based on a phase analysis in the frequency domain and thermal
wave conduction [
169
]. These two techniques are, arguably, the most common alterna-
tives. The former can be used to detect several damage typologies, e.g., air bubbles [
170
],
inclusions of foreign matter [
171
], deficiency in adhesive bonding [
170
], and other glue
faults [
172
] in GFRP WT blades. The latter was tested and validated, again on GFRP WT
blades, with flash lamps for deep defects and delaminations [173].
Regarding non-optical pulsed approaches, EC-based pulsed thermography has shown
several successful applications in the last decades, especially for CFRP materials, e.g., for
delamination depth evaluation [
174
], impact damage [
175
,
176
], damage classification [
177
],
etc. However, it remains limited by technical issues such as the lift-off effects [
178
] and
the difficulty to discern subsurface defects in the inner layers from debonding at outer lay-
ers [
179
]. EC pulsed phase thermography was tested on CFRP laminates in Refs. [
180
,
181
].
Microwave pulsed thermography was tested on both CFRP specimens (with artificially-
added damages in Ref. [
182
], for delamination detection in Ref. [
183
], and for comparison
to laser-based pulsed thermography in [184]) and GFRP [185].
For what concerns the other (non-pulsed) active IRT techniques, stepped thermog-
raphy uses discrete increases in the temperature, while modulated thermography uses
frequency-modulated thermal waves. Lock-in thermographic analysis uses periodic input
energy waves to detect and localize internal inhomogeneities. This is achieved thanks to the
interferences between incoming and reflected heat waves; thus, the strategy is similar to ul-
trasonic testing, as will be discussed later. The technique was successfully tested with both
optical and non-optical heat sources. In Ref. [
186
], 1400 W halogen lamps were utilized for
the detection of skin–skin and skin–core delamination in a CX-100 WT blade. A commercial
microwave over was used in Ref. [
187
] with a CFRP specimen. Lock-in vibrothermography
was tested for delamination detection of CFRP specimens in Ref. [
188
]. Vibrothermography
can be also performed with high-energy bursts in the 10–50 kHz range [189].
Finally, line scanning thermography is a NASA patented technique that uses an IR
sensor moving in synchronous with the heat source to dynamically investigate metallic or
composite surfaces. It has been tested for impact damages on CFRP panels [
190
] and to
assess manufacturing defects in GFRP blades [191].
Some relevant studies for both active and passive IRT are reported in Table 7. Most
of the scientific articles encountered in this review discussed SHM applications for WT
Sensors 2022,22, 1627 25 of 52
blades; only a few works discussed applications for rotating machinery components such
as, e.g., fatigue-damaged gears [
192
]. Several of these techniques can be applied to metal-
lic/ferromagnetic components as well, for the structural monitoring of HAWT towers (e.g.,
using pulsed EC to detect corrosion [192]).
Table 7.
Some notable and recent examples of IRT techniques applied for the NDE of wind turbines.
Study Year Technique Notes Application
Rumsey & Musial [193] 2001 Passive IRT
Infrared thermography was applied by
the National Wind Technology Center at
the National Renewable Energy
Laboratory for the testing of full-size WT
blades. One of the tests performed was a
fatigue test in which a cyclic load was
applied to the WT blade until failure.
SHM
Dattoma et al. [194] 2001 Active IRT (external
heating and readings
during the cooling phase)
The IRT procedure was experimentally
tested on a WT blade sandwich panel,
taken from the box spar. Glue infiltration,
water ingress, and skin–core debonding
were tested.
SHM
Hahn et al. [195] 2002 Thermoelastic stress
analysis
Used to monitor the stress distribution
on a GFRP blade during static and
fatigue tests. Strain gauges were applied
as well to assess the integrity of the
root section.
SHM
Cheng & Tian [196] 2011 Inductive IRT (pulsed
eddy current
thermography)
The proposed method is based on
inductive thermography for the
inspection and assessment of
CFRP components.
SHM
Pan et al. [197] 2012 Pulsed eddy current
The inductor and the IR camera were
placed on opposite sides to detect
damage in the heat transmission mode
on CFRP specimens intended for
WT blades.
SHM
Cheng & Tian [198] 2013 Pulsed eddy current
Detected surface cracks, impact cracks,
defects, and delaminations from
transient thermal images or videos on
CFRP specimens.
SHM
Dattoma & Giancane [
199
]
2013 Passive IRT during
fatigue tests
Compared DIC and IRT results on a
GFRP specimen employed for
WT blades. SHM
Galleguillos et al. [200] 2015 Passive IRT from a
UAV platform
Performed in situ surveys on rotating
WT blades (in-service) with passive IRT
from an unmanned rotorcraft. SHM
Gao et al. [201] 2016 Pulsed eddy current
Developed a multidimensional tensor
model based not only on the analysis of a
single physical field such as heat
conduction (conventional approach) but
also on the inclusion of other properties
such as electrical conductivity and
magnetic permeability as well.
SHM
Paulmbo et al. [202] 2016 Lock-in IRT analysis (heat
source: halogen lamps)
The technique was tested for the
debonding of GFRP joints and compared
to ultrasonic testing. SHM
Yang et al. [203] 2016 Pulsed eddy current
Combined eddy current pulsed
thermography and thermal-wave-radar
analysis for the assessment of
delamination on CFRP blades.
SHM
Palumbo et al. [204] 2017 Thermoelastic phase
analysis
The study focused on the fatigue damage
analysis on GFRP specimens, analysing
the thermal signal in the
frequency domain.
SHM
Sensors 2022,22, 1627 26 of 52
5.5.3. Physically-Attached Temperature Sensors
While conventional or advanced IRT are more prominent for blade inspection, tem-
perature changes are often utilized for monitoring rotating machinery components as well.
However, this is generally achieved not by RS but rather by employing physicallyattached
sensors, such as thermocouples or similar electrical devices. Usually, at least three tempera-
ture sensors are installed at the main shaft support bearing and the high-speed shaft bearing,
and for the lubricant oil [
205
]. The operating temperatures of the generator, converter,
and transformer are often monitored as well. These readings are generally included in the
SCADA dataset along with ambient temperature [
206
]; therefore, they are generally pro-
cessed along with several other thermophysical measurements to detect anomalies. Some
noteworthy examples for CM focused solely and/or prominently on temperature readings
can be found in Guo et al. [
59
], Guo & Bai [
207
], Cambron et al. [
208
], and
Astolfi et al. [209]
.
Even more specifically, approaches based on oil temperature measurements were recently
reviewed by Touret et al. [210].
5.6. Radiographic Testing (RT)
Radiography is a conventional approach for the internal inspection of structures and
mechanical systems. In this regard, the most common radioactive sources available for
industrial applications are X- and Gamma-rays. However, the distinction between X-rays
and Gamma rays is not always very clearly defined. Depending on the conventions,
wavelengths between
∼
10
−10
and
∼
10
−12
m are generally considered X-rays. Shorter
wavelengths are classified as Gamma rays. Neutron radiography is also a viable application,
even if there is a very limited number of available examples encountered in the scientific
literature (e.g., in Ref. [211]).
Computed Tomography (CT), specifically, saw a relevant increase in popularity in the
last decade, mainly due to the increased availability of X-rays, improvements in spatial
resolution, and the reduced acquisition time [
212
]. These CT scanners can be 2D or 3D and
portable (hand-held or via manned or unmanned platforms such as in Ref. [
213
]), even if
the largest ones are fixed and available at specific laboratory testing locations. These allow
sections of WT blades up to 4.5 m-long to be scanned in a single take [214].
The main concept is to transmit ionising radiation throughout a dense material, mea-
suring its attenuation along the photon path. For a homogeneous material, the amount
of total attenuation would be a spatial constant, only depending on the thickness and
density of the material. This is visually presented as pixel readouts, generally in a greyscale.
Flaws and density inhomogeneities can be detected and located as anomalies in the image
outputs, with extremely good spatial resolution thanks to the very short wavelengths
utilized. This technology is particularly efficient for voids and other discontinuities that lay
parallel to the ray beam. On the other hand, the energy of photons is proportional to their
frequency (thus, inversely proportional to wavelength). This makes RT one of the most
energy-demanding NDTs. Therefore, it is a relatively expensive technology, and harmful
for biological tissue (since the rays can interfere and damage them at the molecular scale).
Thus, the operators must be adequately protected and the procedure is inherently more
hazardous than other options.
For this and other practical reasons, RT is more common for laboratory experiments
rather than in situ applications. Thus, for wind turbine applications, RT can be better used
for material testing, research on fracture mechanics, and forensic analysis after structural
failure. For instance, Mishnaevsky Jr. et al. [
215
] used an X-ray CT to investigate the
erosion mechanisms on the leading edge of WT blades at a microscopic scale. Jespersen
and Mikkelsen [
216
] and Baran et al. [
217
] used the same technique to investigate, respec-
tively, the evolution of fatigue damage and manufacturing defects (fibre misalignment
and porosities) in GFRP specimens intended for blade manufacturing. For the gearbox
and other mechanical components, an X-ray CT was used on laboratory experiments by
Gould et al. [218]
to map the distribution of White Etching Cracks (WECs) networks within
failed bearings, to assess the effects of subsurface steel inclusions as initiation sites. Gegner
Sensors 2022,22, 1627 27 of 52
& Nierlich [
219
] used an X-ray diffraction-based residual stress analysis on operating WT
gearbox bearings to investigate the effects of vibration loading on WECs.
5.7. Microwave and Terahertz Testing
Microwave and terahertz (THz) testing are based on electromagnetic radiation with
long and very long wavelengths, ranging from 1 mm up to 1 m and beyond. These corre-
spond to frequencies between 100 MHz and100 GHz, for standard microwaves, or 100 GHz–
10,000 GHz (0.1–10 THz) for THz approaches. Differently from the short-wavelength
wave-based approaches seen before (and from ultrasonic tests, which will be discussed
later), microwaves can penetrate dielectric materials and thus interact with their inner
structure with limited signal attenuation [
159
]. THz waves can penetrate even thicker
layers of low- and very-low-conductive polymers such as GFRP and other fibre-reinforced
polymers used for the manufacturing of WT blades (except for CFRP, which is instead too
conductive to be inspectable with THz testing [220]).
Indeed, both microwaves and THz techniques are particularly applied for blade
monitoring. Indeed, the only scientific article of this group that met the selection criteria
(>30 citations as of 1 December 2021) to be considered highly influential was the work
of Kuei Hsu et al. [
221
]. They used time-domain spectroscopy and terahertz radiation
to detect damages inserted by sawing small cuts into GFRP laminates (intended for the
manufacturing of WT blades). Martin et al. [
222
] compared a terahertz inverse synthetic
aperture radar system with X-ray and IRT imaging for the inspection of GFRP WT blades’
spar caps.
Some other recent applications to GFRP WT blades—which also include THz-inducted
active thermography—are reported in Refs. [
223
,
224
]. Applications for defect detection on
both CFRP and GFRP composites are discussed in Refs. [
225
,
226
]. Im et al. [
227
] reported
an application of THz testing to their trailing edge. On the other hand, there does not yet
seem to be any relevant study on the microwave or THz inspection technologies for the
condition monitoring of WT rotating machinery elements.
For HAWT towers, microwave technologies can be used to inspect the metallic surfaces
when covered with epoxy or other insulating paints, as it is often applied for long-term
steel protection in severely corrosive atmospheric conditions. This has been proven to be
feasible in Ref. [
228
] for fire protect-coated steel panels, using frequencies between 8 and
12 GHz. THz waves were also proved to be able to detect corroded metals under thick
insulating layers, plus water intrusion in sandwich panels [
229
]. THs imaging was proven
to provide a higher resolution than, e.g., ultrasound testing; on the other hand, it is limited
by a lower penetration capability [230].
Considering a different approach, Pieraccini et al. [
231
] used a portable, high-speed
continuous-wave step-frequency interferometric radar, which transmits continuous mi-
crowaves (central frequency: 16.75 GHz) at discrete frequency values, to remotely record
the dynamic behaviour of an onshore WT.
5.8. Electromagnetic Testing (ET)
Electromagnetic Testing, especially using the already cited ECs, relies on the use of
changes in the electric conductivity to detect and localize damages in metallic and non-
metallic components. A complete review, accounting for several fields of application, can
be found in Ref. [
232
]. This strategy is well-known and widespread in manufacturing
industries. Thus, these techniques are viable for the tower structure and substructure. For
instance, they are widely used for weld inspection [
233
]; hence, they can be applied for the
inspection of the circumferentially and longitudinally welded connections in the tubular
steel components.
EC testing can be used for conductive composites as well, e.g., for CFRP WT blades,
enabling the detection of both surface and subsurface damages and defects. However, while
the concept has proven to be feasible (see, for instance, Ref. [
234
]), the relatively low conduc-
Sensors 2022,22, 1627 28 of 52
tivity of these composites might compromise the detection accuracy for certain typologies
of damages such as delamination [234], making EC thermography a preferable option.
Pulsed ECs [
235
] have also been used to detect steel corrosion [
236
] and low-energy
impacts in CFRP composites [
237
]. The first application is apt for the monitoring of the
tower structure and substructure, especially in critical areas such as the splash zone. The
second technique can be advantageous for blade inspection e.g., after a bird strike.
Finally, radio frequency EC testing has been suggested for less conductive materials
such as CFRP [
237
], being applied for the detection of fibre misalignment, gaps, and local
polymer degradation [238,239].
However, despite the several potential applications, no SHM approach based solely
on EC seems to have obtained noteworthy attention from the scientific community. Hence,
the preferred use of ECs remains for volume heating and IRT.
Electromagnetism-based strategies are less commonly found for CM, except for some
applications of electrostatic monitoring of WT gearboxes [
240
], often resorting to a sin-
gle [
240
,
241
] or multiple [
242
] oil-line electrostatic sensors. The concept has been tested for
WECs in WT gearboxes as well [243].
5.9. Acoustic Emissions (AEs)
The key concept of AE is that, when an internal crack propagates, it releases energy in
form of acoustic waves. Debonding, delamination, crushing, and other kinds of damage
produce localized, transient changes in the stored elastic energy as well. These elastic
waves are therefore also known as stress release waves [
50
] and travel inside the material.
AE testing is based on their detection. This basic procedure is sketched in Figure 11.
Figure 11. The basic concept of AE event detection.
These acoustic waves are generally too weak to be heard by a human bystander, yet
they can be easily detected from (one or more) sensing devices attached to the surface of
the inspected element. There are, however, two main issues:
(1)
not all the typologies of damage emit strong AE;
(2)
even more importantly, many damage-unrelated phenomena emit AEs.
Therefore, especially for in situ testing, these confounding influences may exceed
crack-related emissions.
Assuming their correlation with damage, once detected, these non-audible emissions
can be then also used to estimate and (with multiple sensors) locate the origin of the damage.
This last task is generally performed via time-of-flight triangulation, even if alternatives
with less than three sensors have been proposed, e.g., in Ref. [244].
In SHM, AE event detection is particularly common for WT blade laboratory testing
since AEs occurring during loading conditions are very likely indicative of the presence
of crack propagation. In a load-hold test [
245
], the WT blade is loaded slightly above the
highest service load and then held in position for around 10 min. For an undamaged fibre
composite structure, AEs will occur only during the first loading and not be re-emitted
significantly on subsequent reloading to the same level. Therefore, sustained emission
Sensors 2022,22, 1627 29 of 52
during a load-hold is considered indicative of damage [
245
]. This was documented as early
as in the 1990s during the loadings of blade fatigue tests [246,247].
The most relevant examples of As applications for WT blade monitoring are included
in the second part of Table 8; some other less cited, but still noteworthy studies include the
research completed by the Centre for Renewable Energy Sources (CRES) on an NM48/750
NEG-MICON WT blade, monitored in-service [248,249].
For CM, the basic concept is that faulty mechanisms (e.g., bearing defects) disrupt the
AE waveform, causing a detectable divergence from the readings under normal operating
conditions. Historically, these changes in the AE signatures have been considered to be
observable earlier than significant alterations in the vibrational signatures of the same
pieces of rotating machinery, thus allowing for early damage detection and prognosis [
250
].
AE techniques have been proved feasible for the monitoring of ball bearings, standard
roller bearings [
251
], and tapered roller bearings [
252
]. Soua et al. [
253
] evaluated their
feasibility for gearboxes and generator shafts. Purarjomandlangrudi & Nourbakhsh [
254
]
tested AEs to detect a fault in the outer race bearing in a low-speed shaft rig test, simulating
the internal components of a WT drive train.
The first part of Table 8reports several relevant studies of the last 20 years for CM.
A comprehensive review of previous works about the AE-based structural diagnosis of
bearing defects, gearbox faults, and pumps can be found in Mba & Rao [255].
Table 8.
Some notable and recent examples of AE techniques applied for the NDE of wind turbines.
Study Year Technique Notes Application
Eftekharnejad & Mba [256] 2009 AE waveforms. Applied for the detection of seeded
tooth root cracks in one helical gear
of the wind turbine gearbox. CM
Elforjani & Mba [257] 2010 Continuous AE energy
monitoring.
The authors applied AEs for the CM
of low-speed shafts and bearings
(separately) also considering
different conditions such as lubricant
starvation. The bearing test
demonstrated the AE’s efficiency in
detecting crack initiation
and propagation.
CM
Eftekharnejad et al. [258] 2011 Kurtogram (spectral
kurtosis).
Compared the effectiveness of
applying the kurtogram to AEs and
for a roller bearing on a laboratory
test bench.
CM
Qu et al. [259] 2012 Time synchronous
averaging (TSA)
and kurtosis.
The heterodyne technique used in
telecommunication was used to
pre-process AE signals, reducing the
sampling frequency from MHz
to kHz.
CM
Niknam et al. [260] 2013 PAC-energy (Physical
Acoustic Corporation
PCI-2 AE system).
This study focused on wind turbine
drive trains subject to rotor
unbalances. These unbalances may
be caused by manufacturing defects
or non-uniform accumulation of ice,
dust, moisture, or even damage on
rotor blades.
CM
Ferrando Chacon et al. [261] 2016
Root Mean Square Error,
Peak Value, Crest Factor,
and Information Entropy
of AE waveforms.
The confounding influences induced
by different operating conditions
(load and torque) on the AE
signature of a wind turbine gearbox
were investigated.
CM
Sensors 2022,22, 1627 30 of 52
Table 8. Cont.
Study Year Technique Notes Application
Zhang et al. [262] 2017
Damage localisation was
performed via
triangulation (delays in
the time of arrival).
The first attempt of mechanical fault
localisation for CM inside a wind
turbine gearbox. CM
Joosse et al. [245] 2002 Load-hold test. An early application of AEs off-site
on a detached WT blade. SHM
Anastassopoulos et al. [263] 2002 Load-hold test.
Machine Learning (specifically,
Unsupervised Pattern Recognition)
was applied to AE data from ten
WT blades.
SHM
Blanch & Dutton [264] 2003
Load-hold, stationary, and
operating tests.
AEs applied on-site to attached
blades (both stationary and rotating
during normal operating conditions).
SHM
Paquette et al. [265] 2007 Three-point bending test.
The article documented a 5-year long
project performed at Sandia National
Laboratories (USA) to characterize
WT blades made of carbon fibres.
SHM
Zarouchas & Van
Hemelrijck [266]2011
Peak frequency analysis of
AEs and Digital Image
Correlation.
AEs were used to characterize the
crack growth at different scales in
laboratory specimens, treated with
an adhesive used for WT blades
composites. Tensile and compression
tests were executed. DIC was used to
compare the strain measurements
with the recorded acoustic activity.
SHM
Han et al. [267] 2013 Static loading test.
AEs and strain measurements of a
WT blade inner shear web were
compared, to correlate acoustic
emissions and stress conditions.
SHM
Bouzid et al. [268] 2014 Ambient
excitation(naturally
occurring AEs).
Proposed a Wireless Sensor Network
(WSN) architecture for damage
localisation in the blades of operating
wind turbines (via triangulation).
SHM
Tang et al. [269] 2016 Pencil lead break test.
The acoustic emissions were
generated by breaking a pencil lead
in the blade surface. Proved the
feasibility of damage severity
assessment and growth tracking.
SHM
Gómez Muñoz & García
Márquez [270]2016
Pencil lead break test.
Damage localisation was
performed via
triangulation (delays in
the time of arrival).
Three macro-fibre composite
transducers were applied on the
surface of a WT blade. SHM
Tang et al. [271] 2017 21-day long fatigue test. Unsupervised Pattern Recognition
was applied to a very large dataset of
recorded AEs. SHM
5.10. Ultrasonic Testing (UT)
Differently from AE testing, where the acoustic waves naturally originate at the dam-
age location and propagate freely throughout the whole structure, in UT high-frequency
waves are generated at a damage-unrelated, user-defined origin. The centre frequency is
generally included between 0.1 and 15 MHz but can reach up to 50 MHz; yet, a variety
of frequencies can be used, allowing optimization for resolution and/or penetration. As
for any portable device, the UT can be performed by manned or unmanned platforms,
such as climbing robots or others. Skaga [
272
] investigated the feasibility of UAV-carried
UT sensors, testing it for WT blade monitoring and comparing the results with hand-held
instrumentation.
Sensors 2022,22, 1627 31 of 52
The source of the ultrasonic waves can be either in contact or not with the target
surface. For contact techniques, most of the conventional transducers are piezoelectric;
thus, they require a wet film (gel, oil, or water) as a couplant between them and the test
object. Water immersion is used for this aim in laboratory testing when the test materials
allow it. Nevertheless, some technologies, such as electromagnetic acoustic transducers,
do not require the use of a couplant. Silicon membranes or other solid couplants can be
used for dry coupling as well, even if they are often limited in the range of frequencies
that can transmit. Air-coupled and laser-borne ultrasonic tests are other viable options
for water-incompatible materials. However, the air has a very low acoustic impedance, so
only a very limited amount of acoustic energy is transmitted. The use of high power pulse
lasers (such as Q-switched Nd: YAG and CO
2
lasers [
159
]) bypasses this issue while also
providing long-range capabilities.
Once emitted, the ultrasonic waves then travel along a well-defined direction through
the material thickness or along its surface (guided wave ultrasonic testing), even over long
distances. In this latter case, generally, slightly lower frequencies are applied
(10 kHz–1 MHz)
.
This allows a much larger inspected area to be covered, also known as the insonified
portion of the tested system. Simple transducers (inclined through a plexiglass wedge)
or ring transducers can be used. Nevertheless, guided waves are slightly less common
than other techniques for wind turbine applications; some applications for NDT can be
found in Refs. [
273
,
274
]. They are often applied for in-service inspection as they can cover
a larger area at once than other UT methods. They have also been recently proposed for ice
monitoring on WT blades [275].
The working principles of conventional and guided wave UT technologies are repre-
sented in Figure 12. Both can be used in reflection mode (with one transducer, analysing the
ultrasound echoes) or transmission mode (with one transmitter and one or more receivers).
The key concept is that when the (volume or surface) wave interacts with an inhomogeneity,
it is partially reflected backwards and partially transmitted forward, with the transmitted
wave being attenuated in its amplitude and delayed in its phase (due to the detour around
the inhomogeneous area or the different propagation velocity inside it). These differences
in amplitude and phase are also quantitatively related to the damage extension [276].
Figure 12.
The basic concept of UT. (
a
) conventional, i.e., through the thickness (reflection mode),
(b) guided waves. (through transmission mode).
Conventional UT can be applied to a wide range of materials. For the tower structure
(and substructure, for offshore installations), it can be used for thickness measurement,
monitoring the risk of material loss due to corrosion. These techniques are even more
important for WT blades due to their composite materials. Indeed, ultrasound waves
are particularly well-suited for fibre-reinforced panels, as the random distribution of the
components in the matrix requires high spatial resolution. However, the presence of such
fibres leads to sound scattering and directional (anisotropic) damping in GFRP, CFRP, and
similar materials [
277
]. The ultrasonic must travel through several centimetres of these
fibre-reinforced polymers; thus, a high voltage ultrasonic pulse is needed to send enough
Sensors 2022,22, 1627 32 of 52
energy into the material. For cross-ply CFRP plates, the effects of lamination and anisotropy
must be considered for damage localisation [278].
Apart from the already-mentioned air-coupled and laser ultrasonics, conventional UT
can be further classified into many categories. These include pulse-echo UT, phased/linear
array UT, local resonance spectroscopy, and others.
In a pulse–echo test, a short-duration ultrasonic pulse is sent into the test specimen
using an ultrasonic transducer. The waves then travel through the specimen and reflect at
the opposite end of the material, in the absence of inhomogeneities, or at cavities (flaws)
and/or discontinuities (delaminations) within the material, if present. The reflected waves
are recorded using the same transducer (in sensor mode). The difference in the travelled
distances results in different times of arrivals (also known as time-of-flight), allowing the de-
fect or damage to be located through the thickness of the inspected structure. The amplitude
and other waveform characteristics can be used as well for further characterisations.
Phased array ultrasonic testing (PAUT) is a growing and very promising technology.
By using many small ultrasonic transducers, each one pulsed independently, it can produce
a quasi-flat ultrasonic beam. This can be steered electronically by changing the time delay
between the transducers, thus allowing different angles to be inspected without physically
moving or turning the portable device. A comparison of advantages compared to standard
UT can be found in Ref. [
279
]. For wind turbine applications, it has been applied for blade
monitoring by the researchers of Sandia National Laboratories [
280
], Lamarre [
281
], and
Zhang et al. [
282
]. However, PAUT is still under development, and several improvements
have been achieved recently (see, for instance, Ref. [283]).
For local ultrasonic resonance spectroscopy, a portion of a large component (e.g., a WT
blade) is excited with ultrasounds in a broadband frequency range. The vibrational response
is recorded with a nearby sensor and is used to obtain the local material, geometrical, and
mechanical properties, for each of the scanning-grid locations [
284
]. This can be seen as the
ultrasound equivalent of the classic hammer and tap tests used for standard local resonance
spectroscopy. This NDT is also viable for nonlinear UT e.g., in presence of breathing
cracks [285].
Other options can be found in the scientific literature. Ultrasonic pulse velocity was
proposed in Ref. [
286
], specifically intended for monopile HAWT foundations monitoring
in combination with other NDE techniques. Single-sided inspection via air-coupled UT
guided Rayleigh waves was proposed in Ref. [
287
] to detect waviness in WT blades. Table 9
reports the main applications found for the timeframe of interest (2000–2021). These are
mostly limited to blade inspection, off- or on-site. UT seems to be rarely considered for
CM; one of the very few mentions can be found in a very recent PhD thesis [
288
] for WT
gearbox bearings.
Table 9.
Some notable and recent examples of IRT techniques applied for the NDE of wind turbines.
Study Year Technique Notes Application
Jørgensen et al. [289] 2004 Ultrasonic immersion test
An early example of UT for the
detection of damages and
manufacturing defects. The skin,
glue, laminate, and sandwich layers
were all clearly visible from
the scans.
SHM
Jasiünienéet al. [290] 2008 Ultrasonic immersion test
with moving
water container
A particular type of ultrasonic
immersion test (contact pulse–echo
immersion testing) was used to
assess internal defects in a WT blade.
The geometry of the defects was
recognized from the ultrasound
images obtained.
SHM
Sensors 2022,22, 1627 33 of 52
Table 9. Cont.
Study Year Technique Notes Application
Raišutis et al. [291] 2008 Air-coupled guided wave
ultrasonic test
The authors used an ultrasonic
air-coupled technique to transmit
guided waves, locating internal
defects in a WT blade.
SHM
Jüngert [292] 2008 Guided wave
ultrasonic test
It compared acoustic waves (from
hammer tests, using local resonance
spectroscopy) with ultrasonic guided
waves. Acoustic waves were found
to be less subject to scattering and
damping while travelling through
the fibre-reinforced material but less
sensitive to small damages (due to
their larger wavelength).
SHM
Jüngert & Grosse [293] 2009 Contact pulse-echo tests
Compared local resonance
spectroscopy (from hammer tests)
with contact pulse–echo UT on
sandwich composites and pristine
and delaminated GFRP. Ultrasonic
waves correctly detected debonding
at adhesive areas.
SHM
Jasiünienéet al. [294] 2009
Air-coupled ultrasonic
tests, ultrasonic
immersion tests with
moving water container,
and contact
pulse–echo tests
UT and radiographic techniques
were compared on WT blade
specimens. The ultrasonic
techniques proved to be more
efficient in terms of implementation
as they only require access from one
side. The best imaging results,
however, were obtained by
combining RT and UT techniques.
SHM
Lee et al. [295] 2011 Long distance laser
ultrasonic test
To overcome the attenuation due to
air travelling, a portable laser-based
device was proposed for
long-distance UT, up to 40 m (indoor
laboratory conditions).
SHM
Park et al. [296] 2013 Long distance laser
ultrasonic test
It proposed a new laser ultrasonic
imaging technique, specifically
intended for rotating blades SHM
Ye et al. [297] 2014 Pulse-echo test
A portable device for 2D (surface)
and 3D (volume) UT scanning was
proposed and tested on GFRP WT
blade specimens.
SHM
Park et al. [298] 2014 Long-distance laser
ultrasonic test
Delamination and debonding were
successfully visualized in a GFRP
composite wind blade structure. SHM
Park et al. [299] 2015 Laser ultrasonic
propagation
imaging system
A two-step UT imaging strategy was
proposed, with an initial coarse
scanning followed by a second
refined one limited to the areas
deemed of major interest after the
first step. Tested on a 10 kW GFRP
WT blade.
SHM
García Marquez & Gómez
Muñoz [300]2020 Macro fibre composite
transducers and
sinusoidal shaped signals
Cross-correlation and wavelet
analysis were applied to detect,
assess, and localize delaminations in
WT blades.
SHM
5.11. Oil Monitoring
Since the early 2000s, oil monitoring and lubricant contamination analyses became
very common techniques for machine condition monitoring, also for WTs. Indeed, their
usefulness is twofold. On the one hand, it is important to assess the quality of the oil,
to prevent mechanical failure due to e.g., lubricant scarcity or solid intrusions (mainly
Sensors 2022,22, 1627 34 of 52
iron or soot particle contamination). On the other hand, several lubricant parameters
are considered indicative of the potential occurrence of mechanical faults (see e.g., the
previous Table 2). These parameters include the oil viscosity (at +40
◦
C and +100
◦
C),
potential water content, wear particles in parts per million (or mg/L), the presence of
dissolved solvents or gases in the lubricant, and the oil acidity/alkalinity (for potential
oxidation) [
301
]. They can be used for the quantitative assessment of the health conditions
of the WT gearbox, hydraulic system, compressor, etc. [
301
]. Specifically, the presence of
wear debris formed in rubbing helps to detect and estimate the severity of wear mechanisms.
Total oil contamination can give general information on oil lubricity [
302
]. Surface fatigue
damage of bearing and gear rolling elements, bearing spalling, and gear teeth pitting are
typical mechanical failures of WT gearboxes that result in the release of metallic debris
particles in the oil lubrication system [
303
]. In this regard, oil monitoring is quite well
developed and codified; e.g., ISO 4406 provides proper thresholds for solid particle content
according to their size distribution ((
≥
4
µ
m/
≥
6
µ
m/
≥
14
µ
m). Table 10 reports the most
influential publications in the field of WT oil monitoring from the last 20 years.
The scientific literature also reports several examples of sensors developed for oil
monitoring. These cannot be reported here in full due to space concerns; one can refer
to Hamilton & Quail [
304
] for a dedicated overview. Only as an example, a couple of
noteworthy publications may be recalled. A microacoustic sensor was proposed in [
305
] for
oil viscosity monitoring; Mignani et al. [
306
] applied wide-range absorption spectroscopy,
fluorescence spectroscopy, and scattering measurements to estimate the oil acidity, presence
of water infiltrations, and phosphorus content. This latter parameter is considered a proxy
of wear since it is generally utilized in anti-wear additives but then absorbed by metallic
surfaces over time.
Table 10.
Some common typologies of oil monitoring strategies encountered in the scientific literature.
Study Year Technique Notes Application
Myshkin et al. [302] 2003 Optical ferroanalyzer
The document presented the
operating principle of the optical
ferroanalyzer, a sensing device for
the estimation of total lubricant oil
contamination, for
condition monitoring.
CM
Dupuis [303] 2010 Oil debris monitoring
The technique is based on counting
debris particles and measuring their
size to assess the severity of the
gearbox failure.
CM
Zhu et al. [301] 2013 Several sensing devices
A total of 10 sensors and
6 performance parameters related to
oil oxidation, water contamination,
and particle contamination
were discussed.
CM
Coronado &
Kupferschmidt [307]2014
Water content, particle
concentration, particle
count, dielectric constant,
viscosity, oil colour, and
oil density sensors
The paper mainly described a highly
accelerated stress screening test
chamber to assess the performance
of oil properties sensors under
extreme ambient temperature and
vibration levels. The oil parameters
are intended as considered as proxies
of wind turbine gearbox conditions.
CM
Zhu et al. [308] 2015 Particle filtering, plus
viscosity and dielectric
constant sensors
Related to the previous paper by the
same authors [301], it applied online
oil monitoring for fault detection and
remaining useful life prediction.
CM
Sheng [309] 2016
2.5-MW dynamometer test
facility at U.S. National
Renewable Energy
Laboratory (fully
described in Ref. [310])
The laboratory tests were performed
on full-scale wind turbine gearboxes
in three configurations: run-in,
healthy, and damaged conditions.
CM
Sensors 2022,22, 1627 35 of 52
5.12. Static Strain Measurements
Static and dynamic strain measurements are both widely utilized for SHM purposes,
especially for monitoring the blade static and dynamic response. The specific uses for
vibration recordings will not be discussed here; the use of displacement time histories
is not different from the one of velocity or acceleration time series. Static strains, on the
other hand, can provide insight about the deflection of the WT blades. During laboratory
testing, this is useful to characterize the operational and failure behaviour of the specimens.
On-site, this allows to both monitor their shape (to avoid collisions with the tower) and to
estimate their stress field.
The sensors are generally deployed at the locations of maximum strain, i.e., close to
the clamped cross-sections, that is to say, at the blade roots (potentially on both surfaces
and directed in both the flap- and the edge-wise directions, totalling four sensing devices)
and at the bottom of HAWT towers.
Optical fibres have been extensively investigated and applied for WT strain monitoring.
This derives from their several advantages, such as their immunity to electromagnetic
interference and their good accuracy.
In particular, fibre Bragg grating (FBG) sensors are commonly used for strain mea-
surement, especially for WT blades. These can be interrogated with different types of
optoelectronic instrumentation. They are advantageous since have the same size and me-
chanical properties as the original fibre. They can be placed in series, performing many
measurements along a single fibre (multiplexing). Deploying many FBG sensors in parallel
and perpendicular lines allow, e.g., to detect and track crack growth or to locate an impact.
Other similar devices include microbend fibres, which were proposed for crack de-
tection in adhesive joints [
48
,
311
], and transverse optical fuses, proposed in Ref. [
312
] for
low-energy impact damage detection in laminated panels. The downside is that fibre-optic
methods are still an expensive technology and are difficult to implement en masse.
Strain memory alloys have been proposed for strain measurement and shape sensing as
well. Verijenko & Verijenko [
313
] suggested their use for SHM, also for WT blades. For this
application, they can be conveniently embedded into the laminate during manufacturing.
On the one hand, the scanning for magnetic susceptibility, needed to enable this technique,
is labour intensive. On the other hand, these systems can be deployed as actuators as well
for aerodynamic load control [314], thus utilized for both tasks at different moments.
To conclude, some high-impact examples of applications based on strain measure-
ments are reported in Table 11.
Table 11.
Some notable and recent examples of strain measurements applications for wind turbines.
Study Year Notes Application
Papadopoulos et al. [315] 2000
An early study on the feasibility of static strain
measurements for WT blades. The main
potential causes of error were discussed and
their impact was experimentally estimated.
SHM
Kim et al. [316] 2011
FBG sensors were embedded into a 1/23 scale of
the 750 kW composite blade to evaluate
its deflection. SHM
Dimopoulos et al. [317] 2012
The authors used strain measurements from
strain gauges to experimentally investigate the
buckling behaviour of the thin steel cylindrical
shells which make up the HAWT tower.
SHM
Choi et al. [318] 2012
FBG sensors were applied to estimate the static
tip deflection of a 100 kW GFRP blade. This
shape sensing is intended to avoid potential
collisions with the nearby tower.
SHM
Sensors 2022,22, 1627 36 of 52
Table 11. Cont.
Study Year Notes Application
Kim et al. [319] 2013
Similar to Choi et al. [
318
], the authors suggested
installing FBG sensors at the bonding line
between the shear web and spar cap SHM
Sierra-Pérez et al. [320] 2016
Compared strain measurements taken from
strain gauges, FBG sensors, and Optical
Backscatter Reflectometer (OBR) sensors on a
prototype GFRP WT blade.
SHM
5.13. Other NDE Approaches
Other less-common NDE strategies and/or applications include:
I.
Dynamometer testing, performed off-site on the whole drive train system, to
assess for potential slipping behaviour in the high-speed shaft tapered roller
bearings [321];
II.
Sound-based monitoring, using audio speakers to ensonify the internal cavities
of WT blades and arrays of external microphones to detect pattern changes in the
airborne sound radiation [322,323];
III.
Short-Range Doppler Radar, very recently tested for the son-site SHM of WT
blades [324];
IV.
Multi-sensor apparatuses, such as e.g., the one proposed in Ref. [
325
] (with optical,
acoustical, and vibrational sensing devices) to detect bird and bad strikes.
plus several other techniques that, however, are limited to very few or even only one
peer-reviewed scientific articles. These NDE methods are less established than their more
classic counterparts reviewed previously. In some cases, this is due to their (relatively)
novelty. All these techniques present several interesting qualities but are hampered by
specific limitations as well.
6. Discussion
Tables 12 and 13 summarize the main points of this review, reporting the most common
application for each NDT (indicated by
X
in Table 12) and their main advantages and
limitations (Table 13). This discussion is only limited to the NDT reviewed here; for many
applications, please remember that in many cases, vibrational and SCADA data analyses are
considered convenient and effective alternatives. These are not included in this discussion
and are postponed to a more detailed future work.
Table 12.
Most common monitoring strategies for the different load-bearing and rotating components
of a wind turbine, according to the literature review (in particular [
61
,
326
,
327
]) and considering both
on- and off-site (laboratory) inspection.
SHM CM
Tower Foundations Blades Bearings Shaft Generator Gearbox
VI X X X (limited visibility) X(limited visibility) X(limited visibility)
Optical
measurements X X
Shearography X
IRT X X X X X X
Temperature,
non IRT X X X X
X-ray CT X X X X
ET X X (CFRP only) X
AEs X X X X X
UT X X X
Oil Monitoring X X X
Static strain X X X
Sensors 2022,22, 1627 37 of 52
Table 13. Advantages and disadvantages of each NDT strategy.
Method Advantages Disadvantages
VI
Non-contact
Very simple
Low cost
Does not require extensive training or
specific instruments (man-made VI)
Can be automated (Computer Vision and
autonomous unmanned platforms)
Limited to surface damages and defects.
Safety hazard for the personnel
(if man-made).
Low accuracy and highly subjective
(if man-made).
Optical Measurements and Shearography
Non-contact
Full-field
Relatively fast to perform
High sensitivity to damage
Shearography requires a specific (and
expensive) setup.
Difficult to quantify the extension
of damage.
Some techniques (e.g., DIC) require
surface treatment.
IRT
Non-contact (except vibrothermography)
Full-field
Relatively fast to perform (except lock-in
thermography; depends on the thickness
of the material for pulsed and EC pulsed
thermography)
High sensitivity to damage
Many options (surface and volumetric
heating, different inputs, etc.)
Relatively simple setup (except
microwave thermography)
Highly standardized (e.g., ISO
10880:2017)
Good spatial resolution (depends on the
specific option)
Active IRT requires an active source
of heating.
Only microwaves ensure uniform
volumetric heating.
Only microwaves and
vibrothermography allow
selective heating.
Only lock-in and pulsed phase
thermography are
emissivity independent.
Surface heating thermography is limited
to the outermost layers of the material.
Eddy currents cannot be applied to all
materials (depending on
their conductivity).
Pulsed phase thermography requires
extensive signal processing to analyze
the results.
Damage-unrelated factors may cause a
rise in temperature.
Cannot provide a very accurate
damage diagnosis.
Temperature, non IRT Highly standardized (e.g., ISO
15312:2018).
Requires an embedded sensor (subject to
sensor faults).
Damage-unrelated factors may cause
temperature rise.
X-ray CT Non-contact
Very high spatial resolution Radiation hazard
Complex (and expensive) setup
ET Non-contact Relatively low-cost. Sensitive to lift-off
Limited by the material conductivity
Requires specific instruments.
AEs
Passive (no input required)
Able to detect early-stage cracks and
small defects.
Can be applied on-site and in-service
Can be applied also to low-speed
rotating machinery.
Can cover relatively large areas/volume.
High signal-to-noise ratio.
Frequency range far from
load perturbation.
Relatively expensive.
Requires a very high sampling rate.
Acoustic wave attenuation in the material.
Only detect damages at their inception or
during their growth.
Difficult to quantify the extension
of damage
In general, very noisy and difficult
to interpret.
Sensors 2022,22, 1627 38 of 52
Table 13. Cont.
Method Advantages Disadvantages
UT
Can be applied on-site and in-service
Many options
Can cover relatively large areas/volume,
also with complex geometries
Require an active source of ultrasounds
Coupling issues (especially for
water-incompatible materials)
Ultrasound attenuation in the material
The analysis of the results requires an
expert user
Oil
Monitoring
Easy to install.
Enables the direct characterisation of
several oil parameters.
The results are easy to interpret
Only viable for mechanical systems with
a closed-loop oil supply system.
Expensive for continuous
online monitoring.
Static strain
Can provide both damage detection and
shape sensing capabilities.
Conventional strain gauges are easy
to install.
Can be used to monitor dynamic strain as
well (for vibration-based inspection).
Fibre optics are still expensive and
difficult to install.
Regarding the prevalence of these techniques. as reported in Figure 13 (adapted from
Ref. [
328
], based in turn on data from several sources [
329
–
331
]), visual inspection remains
the most widespread approach. Temperature analysis is also very common, most probably
due to the very low cost of physical temperature sensors. UT, AE, and IRT are all (in
general) more expensive and thus less frequently deployed. Therefore, it is understandable
that the trend is quite clearly decreasing with increasing operational costs. Permanent
monitoring apparatuses, on the other hand, represent an outlier relative to this general
trend. The reason behind this larger-than-expected deployment despite the higher costs
derives from the perceived usefulness of embedded systems. Moreover, the total costs
account for both the installation—which is relatively expensive for a global system—and
the operating expenses, which instead are relatively convenient. Thus, a vibrational-based,
ML-based SHM apparatus becomes more and more convenient throughout the years, when
the structure/system as well becomes more prone to damage due to ageing and normal use
and consumption. However, as mentioned in Section 3.5, global and continuous monitoring
is intended to enable condition-based maintenance. Therefore, it is not an alternative but
rather a complement to the local damage assessment capabilities of the NDE approaches
reviewed here.
Figure 13.
Qualitative distribution of costs and deployment levels of different NDTs. Based on data
from Refs. [329–331].
Sensors 2022,22, 1627 39 of 52
7. Conclusions
Despite the great achievements and growth of the wind industry in the last decades,
the reliability of wind turbines is challenged by premature mechanical faults, blade failures,
and even structural collapses. These issues involve both on- and offshore installations,
isolated or grouped in large and dense wind farms. Their consequences are, however, even
greater for offshore wind farms, due to the logistic of maintenance in the open sea.
Structural Health Monitoring (SHM) is concerned with the overall load-bearing struc-
ture of an asset, while Condition Monitoring (CM) is focused on the fault detection within
the subsystems and components of rotating machinery. Therefore, both are essential for the
correct functioning of wind turbines.
Here, all the main Non-Destructive Techniques (NDTs) and Evaluation (NDE) strate-
gies for wind turbines SHM and CM have been reviewed and thoroughly discussed. This
included the findings of more than 300 documents published in the last 20 years. These
covered all the related aspects, for the convenience of both academic researchers and
industry practitioners.
Overall, it is evident that no single option is superior to the others. The synergies of
contact and non-contact measurement techniques, especially when applied to the estimation
of different physical quantities, should be preferred. Therefore, the main conclusion is that
a large set of different sensing techniques can balance out the limitations and drawbacks
of each single NDT. This allows detecting damage/fault-related anomalies, as well as
discerning them from unrelated operating and environmental variations. This holistic
approach can provide a significant economic and safety benefit to the wind industry.
The NDE approaches reviewed here must be seen in the broader context of Intelligent
Maintenance. Thus, their use should be integrated with permanent monitoring apparatuses,
embedded in the structure and mechanical components and eventually integrated within a
Supervisory Control And Data Acquisition (SCADA) system. These apparatuses, which
are becoming more and more widespread and standard in the industry, will be reviewed in
future works.
Author Contributions:
Conceptualization, M.C. and C.S.; methodology, M.C. and C.S.; writing—
original draft preparation, M.C.; writing—review and editing, C.S.; visualization, M.C.; supervision,
C.S.; project administration, C.S. All authors have read and agreed to the published version of
the manuscript.
Funding: This research received no external funding.
Institutional Review Board Statement: Not applicable.
Informed Consent Statement: Not applicable.
Data Availability Statement:
All data reported in this review are available at the referred original sources.
Acknowledgments:
The authors would like to thank Calogero Bonetti for his precious help and support.
Conflicts of Interest: The authors declare no conflict of interest.
Abbreviations
The following abbreviations are used in this manuscript:
AE Acoustic Emission.
AI Artificial Intelligence
BVID Barely Visible Impact Damage
CFRP Carbon Fibre Reinforced Polymer
CM Condition Monitoring
CT Coherence Tomography
DIC Digital Image Correlation
EC Eddy Current
Sensors 2022,22, 1627 40 of 52
ET Electromagnetic testing
FBG Fibre Bragg Grating
GFRP Glass Fiber Reinforced Polymer
HAWT Horizontal Axis Wind Turbine
IRT Infrared Thermography
LCOE Levelized Cost of Energy
LDV Laser Doppler Velocimeter
NDE Non-Destructive Evaluation
NDT Non-Destructive Technique
OCT Optical Coherence Tomography
O&M Operation and Maintenance (cost)
RS Remote Sensing
RT Radiographic Testing
SCADA Supervisory Control And Data Acquisition (system)
SHM Structural Health Monitoring
SHT Surface Heating Thermography
UAV Unmanned Aerial Vehicle
UT Ultrasonic Testing
VBI Vibration-Based Inspection
VHT Volume Heating Thermography
VI Visual Inspection
WEC White Etching Cracks
WT Wind Turbine
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