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Nowadays in industry there is a great and increasing demand in resource management, taking into consideration the ever-growing complexity of technical systems. The concept of maintenance is one the most important topics of product development today. As the factories and the industry evolves, the need of proper maintenance plays a major factor in cost and efficiency optimization. In this paper a state of the art of maintenance techniques is presented, predictive maintenance being one of the biggest topics going forward. Predictive maintenance techniques are discussed and presented in detail creating the necessary links with nowadays industry advances: Industry 4.0.
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IOP Conference Series: Materials Science and Engineering
A state of the art of predictive maintenance techniques
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ACME 2020
IOP Conf. Series: Materials Science and Engineering 997 (2020) 012039
IOP Publishing
A state of the art of predictive maintenance techniques
P Coandă1, M Avram1 and V Constantin1
1Mechanical Engineering, Mechatronics and Precision Mechanics Department, University
Politehnica of Bucharest, Bucharest, Romania
Abstract. Nowadays in industry there is a great and increasing demand in resource
management, taking into consideration the ever-growing complexity of technical systems. The
concept of maintenance is one the most important topics of product development today. As the
factories and the industry evolves, the need of proper maintenance plays a major factor in cost
and efficiency optimization. In this paper a state of the art of maintenance techniques is
presented, predictive maintenance being one of the biggest topics going forward. Predictive
maintenance techniques are discussed and presented in detail creating the necessary links with
nowadays industry advances: Industry 4.0.
1. Introduction
The present paper aims to deal with the main maintenance concepts, focusing on the principle of
predictive maintenance.
The industrial revolution represents one of the most important moments in human history and
continues to play an important role in the modern world today. The main areas targeted by the
industrial revolution were the technological, socio-economic and cultural ones. Through the changes,
it has shaped the modern world in the way it is today, changing the way people live, how they spend
their free time and not least how the current political class is organized globally.
Some of the most important technological changes targeted by the industrial revolution are:
Use of new materials, mainly iron and steel;
Use of new energy sources, such as coal, steam engine, electricity, oil and internal combustion
Inventing new machines for the textile industry (eg torsion wheel);
Organizing the production in the factory type system that we meet today and the division of
Development of transport and means of communication such as: steam locomotives, steam
vessels, airplanes, telegraph, radio;
Application of scientific principles in industrialization.
All the technological changes have greatly contributed to the use of natural resources and have
enabled the mass production of goods [1]. Today, maintenance is an issue of the utmost importance in
the industrial environment and beyond. Maintenance performed correctly, regardless of its type, does
nothing but improve the working environment and implicitly increase the performances, having a
general effect. In the following chapters, maintenance concepts will be dealt with extensively, as well
as topical solutions in this field.
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IOP Conf. Series: Materials Science and Engineering 997 (2020) 012039
IOP Publishing
2. The maintenance concept
When we talk about products, they represent the centre of interest in companies and factories around
the world, therefore a product management plan is needed. It ensures good development and
organization of all stages, resources and means involved in the production process.
The idea of maintenance is present from the design stage of a product. It must ensure the proper
functioning of the product according to its life. As can be seen in figure 1, according to [2], there are
five stages in the life cycle of a product: idea, definition, realization, use, recycling. These five phases
can be divided into 3 broad categories:
1. The incipient phase: idea, definition, realization;
2. Middle phase: use;
3. Final phase: recycling.
Figure 1. The lifecycle of a product.
As time goes on, the medium phase lasts the longest, the product being in the use phase, so it is
necessary to maintain it in order to function in optimal parameters.
The maintenance concept is defined by the standard EN 13306, as follows [3]: the combination of
all technical, administrative and managerial actions performed during the life cycle of an element to
maintain or restore the conditions under which it can perform the required function. In order to
perform the maintenance operation, a maintenance management plan is required. Maintenance
management is defined as the sum of all the management activities that determine the maintenance
objectives, strategies and responsibilities and their implementation by means of maintenance planning,
maintenance control and improvement of maintenance activities.
There are therefore several types of maintenance, also defined in standard EN 13306. Figure 2 shows
the schematic representation of the maintenance types.
Figure 2. Types of maintenance according to EN 13306 standard.
Equipment maintenance has evolved over time, from maintenance that was performed only when
the elements suffered damage to modern methods, proactive and predictive maintenance, the latter
being the most popular today [4]. Maintenance performed only in case of damage has proven to be an
inefficient method in time because in many cases alternative failures are observed in the system. This
has led to the adoption of the principle of scheduled maintenance where the equipment is subject to a
total overhaul whether it has problems.
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IOP Conf. Series: Materials Science and Engineering 997 (2020) 012039
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The disadvantage of the scheduled maintenance method is that it analyses the service life of each
element in the system, so some elements are changed even if they have not reached the end of their life
cycle. This has led to the evolution towards predictive maintenance.
Figure 2 shows a generalized classification of maintenance types. According to [5], maintenance tasks
can be classified into two broad categories:
1. Reactive maintenance - the system is used until breakdowns occur. When a fault occurs, the
damaged elements are replaced in order to restore the operating capacity of the system;
2. Proactive maintenance - maintenance actions are planned or take place as a result of tracking
indicators. Planning can be done using sensors that announce the early stages of a fault. This
type of maintenance aims to maintain the functionality of the system;
Regardless of the type of maintenance applied, the purpose of these activities is to reduce the stops
caused by damage as much as possible. To be able to minimize the stops, it is necessary to understand
the failure modes and mechanisms. Table 1 presents a comparative analysis of advantages and
disadvantages for reactive and proactive maintenance.
Table 1. Reactive and proactive maintenance advantages and disadvantages.
Low initial costs
Easy to implement
Increased system reliability
Minimizing logistical stops
Reduction of non-scheduled
Lowers costs (optimizing
parts, optimizing work)
Maintenance planning
Optimization of logistic
3. Predictive maintenance nowadays
Predictive maintenance is a relatively new concept that can be framed as a proactive maintenance
approach. When speaking about predictive maintenance techniques, conditional predictive
maintenance is one of the most used techniques. Conditional predictive maintenance is a strategy
where maintenance is performed by observing certain parameters or certain components of the system.
The advantage of this method is that the state of the system is presented in real time, based on the
parameters followed. Although the systems generally have a well-defined operating curve by the
manufacturer, depending on the operating conditions and the working environment it may undergo
changes, as a result, damage may occur earlier. At the same time, this method increases the ease with
which the faults are detected and especially, their place.
Regarding conditional predictive maintenance, there is a wide range of parameters that can be
monitored for predictive maintenance, some of the most important being:
Vibration analysis - represents the most efficient method for detecting problems in the
equipment that perform rotational movements;
Acoustic analysis - this can detect or monitor cracks in pipes or pipes;
Lubrication oils analysis - the particles found in the oils used to determine the degree of wear
of the components are analysed;
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Particle analysis in the working environment - a method generally used in equipment working
in a fluid environment;
Corrosive analysis - ultrasound measurements are performed to determine the corrosion in
different structures;
Thermal analysis - used especially in the case of mechanical and electrical systems to detect
overheating in general.
Performance analysis - an efficient technique to determine operational problems in the system;
Using one or more of the above methods, maintenance solutions for equipment and systems are
developed. Therefore, considering that more than 30% of maintenance costs are caused by faulty
maintenance planning [6] leading to added costs in the production process, predictive maintenance is a
method of increased interest especially when considering industrial environments, where stops can
cause large losses.
A new approach to in the predictive maintenance techniques is represented by the usage of 4.0
Industry standards in order to facilitate the processes contained by the predictive maintenance
technique. As nowadays the industry is facing difficulties due to the development of technology, the
diminution of natural resources, the incidence of wars and natural disasters and at the same time,
social issues such as globalization and increasing the retirement age for the labor force, represents
difficulties that produce substantial economic effects [7]. In addition to the points listed above, it
should be noted that consumers today want a variety and high quality of products, as well as quality
services during use.
The vision of Industry 4.0 comes with a massive impact on the current way of working in the
industry characterized by a new model of socio-technological interaction. These new smart factories
have small, decentralized networks that act autonomously being able to make decisions in different
situations and which are interconnected globally with respect to the company, resulting in intelligent
products that can be tracked in real time and of whose properties and characteristics are known
throughout the production process. The benefits of Industry 4.0 can therefore be used to develop a
predictive maintenance strategy using the aforementioned technologies. They provide the premises for
the implementation of predictive maintenance strategies, this being achievable with reduced material
and human resources.
A brief definition of Industry 4.0 can be considered the one provided by [8] which says that
Industry 4.0 represents basically a synergy between Internet of Things, Internet of Services and of
course, the industrial process. It should be also noted that Industry 4.0 is shaping not only the
manufacturing and maintenance processes, but also the way human-machine interaction take place.
The benefits of predictive maintenance in comparison with scheduled maintenance are srtrongly
highlighted in paper [9]. In comparison with classical maintenance approaches with are at least in the
industrial field the main maintenance techniques applied, predictive maintenance promises to lower
the increasing failures caused by the incapacity to provide a time estimation for components failure.
Regarding predictive maintenance, it can be split in three basic techniques:
Maintenance based on existing process sensors, which are already equipped;
Maintenance based on test sensors;
Maintenance based on test signal technique;
All of the above maintenance techniques aim to provide an improvement in failure analysis, yet they
represent different approaches. The first two techniques can be considered as passive and the third
as active, the implementation considering loop responses and real time testing.
Considering that the vast majority of industrial processes are conducted using electrical and
mechanical systems, paper [10] proposes an overview over predictive maintenance techniques used for
performance enhancements. The main topics considered are electrical motors performance and bearing
failures, these being the most common problems. The study concentrates on two case studies carried
on a 75-hp blower motor and a 200-hp air compressor motor in an active plant. Results and
conclusions were gathered from both experiments, confirming a misalignment in blower motor shaft
and a performance loss and a degradation for the compressor motor due to small periods of overload.
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Grall, Antoine, et al. [11] propose a method of predictive maintenance used for systems that are
subject to continuous deterioration. The paper aims to treat two main points, as follows:
Creating a structure for implementing conditional predictive maintenance and determining a
mathematical model of cost analysis from the way the faults appear;
Demonstrate that long-term maintenance costs can be substantially reduced by using such an
A random model is used to simulate the continuous deterioration of the system. It can simulate an
aging in the case of mechanical models, the evolution of defective products in the case of a production
line, the level of corrosion / erosion in the case of structures. The chosen model offers a cost efficiency
considering the time required to perform maintenance for an indefinite period. It aims to make a
minimum estimation of the costs considering the level of wear and the appropriate moment for
stopping the equipment. As maintenance based on continuous parameter monitoring performs better
than other maintenance methods, the mathematical model presented must be able to prevent random
occurrences. The solution is outlined around two main ideas:
Determining the maintenance method for the system concerned according to the evolution of
the damage (implementation of the strategies of preventive maintenance, corrective
maintenance or the use of the system regardless of the degree of damage);
Determining and establishing the data for the next inspection;
The simulations performed offer a wide range of solutions for determining the maintenance mode
or the use of the equipment until an imminent failure to obtain the desired result: the efficiency of the
maintenance costs.
An innovative approach is also taken by Wang, Jinjiang, et al. [12] who propose a predictive
maintenance system that is based on a new cloud architecture that uses a mobile client instead of the
classic server-client architecture. This approach aims to increase the flexibility and adaptability of the
system, reduce the volume of raw data that is transmitted and improve the response time to the
dynamic changes that occur in the monitored systems. This approach is not without problems, some of
the points that remain open are: automatic maintenance planning, energy management, management of
data analysis and transmission, data storage, large-scale system implementation. To address some of
these issues, as mentioned from the outset, this solution proposes using mobile agents to distribute
tasks in the cloud.
The mobile agent is a method derived from the field of study of artificial intelligence that aims to
divide the tasks and their parallel execution. Agents are represented by software programs that migrate
within the network. The main features of a mobile agent are autonomy, intelligence, adaptability,
which makes this approach suitable for cloud architecture. Such an implementation makes the
distribution of computing power more efficient, reduces the amount of data transmitted and allows the
modification and adaptation of algorithms running on equipment, thus increasing their versatility.
For system testing, six AC motors were used, five of them having different defects, and one being
used as a standard. Using mobile agents, software programs are sent to be run and to purchase
different parameters, according to predictive maintenance methodology. The data is then processed
with specific algorithms, after which a characteristic of the monitored equipment is obtained. The
results confirmed the efficiency of the system and provided relevant data on the condition of the
analysed engines compared to the standard engine.
In the paper [13], the authors propose a method of predictive maintenance that is performed with a
monitoring system subject to errors. For data generation, it is considered a system subject to
continuous degradation which is modelled using Markov chains. This method provides the means for
performing an efficient simulation to track the evolution of the main topic of this paper: correlating the
efficiency of data analysis and acquisition equipment with reducing costs in predictive maintenance
compared to other maintenance methods.
For a robust evaluation of the proposed system, the analysis is performed on 2000 simulated
elements, and the costs of procurement equipment used in predictive maintenance are also considered.
Of course, depending on the quality of the sensors and the cost of repairing the damage, predictive
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IOP Conf. Series: Materials Science and Engineering 997 (2020) 012039
IOP Publishing
maintenance may or may not be the best option. When the simulation parameters are changed and the
quality of the sensors used for the acquisition decreases, the efficiency of the predictive maintenance
decreases considerably, being below the preventive maintenance. This can be solved only by using
sensors and modules for the acquisition and interpretation of qualitative data and also by implementing
filters for the acquired data.
The results of this simulation reinforce the idea of adopting predictive maintenance systems, which
improve maintenance costs, even when the cost of analytical equipment is taken as long as the sensors
and the acquisition systems provide qualitative data.
Ullah, Irfan, et al. [14] propose a predictive maintenance solution through thermography using ML
(Machine Learning) techniques. This type of anomaly occurs mainly in power equipment, where
imperfect contacts, corrosion, imperfect insulation, etc. causes overheating of the equipment. The use
of thermography is a non-invasive method that can monitor the equipment in operation. The analysis is
done on a total of 150 photos, which are determined around 300 points of interest. Then using ML
techniques, the points of interest in the photos are classified as defects or functioning in parameters.
This method is possible using an infrared thermographic camera. It captures and filters the infrared
light component and thus provides images on the thermal profile of the equipment being tracked. As in
the deterioration of the electrical equipment the main effect of the aging of the cables is represented by
an increase of the resistance, the thermographic analysis with infrared can offer relevant information
in the study of the equipment for the implementation of the practices of predictive maintenance.
Regarding the maintenance applied to electrical equipment, it is generally neglected, using the strategy
of use until failure and applying the corrective maintenance when the equipment requires it.
Using the means of Industry 4.0, Cachada, Ana, et al. [15] propose an innovative predictive
maintenance structure based on OSA-CBM (Open System Architecture for Condition Based
Monitoring) architecture. The OSA-CBM structure consists of six steps in comparison to the structure
proposed in this paper which consists of only four steps, as follows:
1. Data acquisition - is based on automatic, semi-automatic and manual data acquisition methods.
This is done using IoT (Internet of Things) and HMI (Human Machine Interface)
2. Offline data analysis - this step is based on the use of several technologies such as machine
learning, cloud technology and advanced data analysis technology to make new monitoring
rules and procedures. Depending on the type of method and algorithms chosen, results with
different degrees of efficiency are obtained;
3. Dynamic monitoring - it is composed of two subassemblies: the visualization subassembly and
the early damage subassembly;
4. The module for decisions and support - when it is necessary to carry out tasks or interventions,
the information is sent to the module for decisions and support through an organizing tool;
The solution comes with improvements in comparison with OSA-CBM technique which makes
usage of the Industry 4.0 and cloud techniques in order to create an enhanced predictive maintenance
Implementing predictive maintenance on an industrial basis represented by Industry 4.0 [16],
changes the processes in comparison with classical maintenance. One of the main problems is
represented by the overwhelming amount of data which is acquired and must be processed accordingly
[17]. This leads to big data paradigm, which must be treated using data management methods.
Data management processes have been around for a long time, yet new techniques are developed in
order to adapt to data environment changes. Data management processes aim to provide a standard
procedure for data analysis in order to evaluate and propose a solution based on analyzed data [18].
There are of course many data processes available [19], each suited to different data management
necessity, some of the most important ones being:
Cross Industry Standard Process for data Mining (CRISP-DM) process model;
Sample, Explore, Modify, Model and Access (SEMMA) process model;
Team Data Science Process (TDSP) methodology;
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IOP Conf. Series: Materials Science and Engineering 997 (2020) 012039
IOP Publishing
The first two models are around for about two decades, but the changes in the industrial processes
data analysis shaped around the new industries asked the necessity for new data management
processed, thus newer methods like TDSP emerged.
Figure 3 presents an intelligent predictive maintenance system framework implemented with
Industry 4.0 means [20].
Figure 3. Predictive maintenance in Industry 4.0 framework.
All of the above methods represent a novel approach in terms of predictive maintenance
techniques. As expected, the predictive maintenance started to become the standard in maintenance
techniques due to the multitude of benefits. With the new technological advances and the 4.0 Industry
movement, the factories and machines started to become more advanced and connected. This opened
the way for real time parameter monitoring which is the founding pillar in the predictive maintenance.
The speed and ease wherewith the data is now acquired and sent is something that must be taken
advantage of. With new innovations, like Internet of Things and Internet of Services new challenges
appear, data management and processing being one of them [21].
4. Conclusions
This paper starts with the beginning of the industrial revolution and briefly presents the industrial
evolution, but also its problems and changes over time. Initially seen as a means of eliminating
humans from the field of work, technology has transformed the world into what it is today.
Since the beginning of technological upgrades, the efficiency of systems and means of production
has been a priority to increase production and reduce costs. The maintenance of the equipment was a
problem that raised a major interest, being a broad topic on which continuous research was carried out.
Today, evolution allows the creation of intelligent and autonomous maintenance systems, which have
analysis capabilities far beyond those of human personnel.
The main subject of the present paper is predictive maintenance - an intelligent way of monitoring
the parameters of the equipment in real time in order to determine the possibility of damage. Further,
the concept of predictive maintenance in Industry 4.0 is presented and how the synergy between these
two revolutionary methods can generate more efficiency. Moreover, data management processes are
presented in order to deal with data acquired in order to implement predictive maintenance system.
The study of the current solutions offers an overview of the research and discoveries in this field.
There are presented several predictive maintenance solutions implemented in different environments
that have different systems analysis methods, use different algorithms, but which have a common
purpose: to determine the possible breakthroughs in advance in order to be able to plan a maintenance
strategy so that the efficiency to be as high as possible.
Therefore, intelligent maintenance started to gain weight in applied maintenance methods due to
the long term benefits it provides. Even if the implementation of Industry 4.0 may be yet in an
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IOP Conf. Series: Materials Science and Engineering 997 (2020) 012039
IOP Publishing
incipient phase, the value added by the means offered by it can provide a starting point for intelligent
maintenance systems and methods which are superior to the old maintenance practices.
5. References
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[2] Stark, John. "Product lifecycle management." Product lifecycle management (Volume 1).
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[3] EN 13306:2010, (2010) Maintenance Terminology. European Standard. CEN (European
Committee for Standardization), Brussels
[4] Scheffer, Cornelius, and Paresh Girdhar. Practical machinery vibration analysis and predictive
maintenance. Elsevier, 2004
[5] Sillivant, D. (2015, January). Reliability centered maintenance cost modeling: Lost opportunity
cost. In 2015 Annual Reliability and Maintainability Symposium (RAMS) (pp. 1-5). IEEE
[6] Park, Chulsoon, et al. "A predictive maintenance approach based on real-time internal parameter
monitoring." The International Journal of Advanced Manufacturing Technology 85.1-4
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[7] Petrasch, Roland, and Roman Hentschke. "Process modeling for Industry 4.0 applications:
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[8] Bartodziej, Christoph Jan. The Concept Industry 4.0. Wiesbaden, Germany: Springer, 2017.
[9] Hashemian, Hashem M. "State-of-the-art predictive maintenance techniques." IEEE
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[10] Lu, B., Durocher, D. B., & Stemper, P. (2009). Predictive maintenance techniques. IEEE
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[11] Grall, Antoine, et al. "Continuous-time predictive-maintenance scheduling for a deteriorating
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[12] Wang, Jinjiang, et al. "A new paradigm of cloud-based predictive maintenance for intelligent
manufacturing." Journal of Intelligent Manufacturing 28.5 (2017): 1125-1137
[13] Curcurù, Giuseppe, Giacomo Galante, and Alberto Lombardo. "A predictive maintenance
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[14] Ullah, Irfan, et al. "Predictive maintenance of power substation equipment by infrared
thermography using a machine-learning approach." Energies 10.12 (2017): 1987
[15] Cachada, Ana, et al. "Maintenance 4.0: Intelligent and predictive maintenance system
architecture." 2018 IEEE 23rd International Conference on Emerging Technologies and
Factory Automation (ETFA). Vol. 1. IEEE, 2018
[16] Li, Z., Wang, Y., & Wang, K. S. (2017). Intelligent predictive maintenance for fault diagnosis
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[18] Ghavami, P. (2019). Big Data Analytics Methods: Analytics Techniques in Data Mining, Deep
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[19] Olson, D. L., & Delen, D. (2008). Advanced data mining techniques. Springer Science &
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... In recent years, several techniques have been developed to monitor and control the desired parameters of the equipment, such as analysis of vibrations, temperatures, pressures, ultrasounds, currents, and voltages [41][42][43][44][45]. Predictive maintenance can be based on the continuous monitoring of certain parameters of a machine or the entire process so that its current condition is monitored in real-time and its future operational state can be predicted [4]. ...
... Although CBM and PdM have been studied in the context of I4.0 in recent years, few researchers have paid attention to this type of maintenance techniques in the automotive industry [49]. Nevertheless, a major issue in production planning of the In recent years, several techniques have been developed to monitor and control the desired parameters of the equipment, such as analysis of vibrations, temperatures, pressures, ultrasounds, currents, and voltages [41][42][43][44][45]. Predictive maintenance can be based on the continuous monitoring of certain parameters of a machine or the entire process so that its current condition is monitored in real-time and its future operational state can be predicted [4]. ...
... That is, CBM involves making decisions about maintenance or repair based on the actual deteriorating conditions of the components [72]. The most advanced and impactful predictive techniques in the industrial sector to date are vibration analysis [45], ultrasound [42], thermography [44], voltage [43], dynamic pressure, and visual inspection. ...
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This article addresses the evolution of Industry 4.0 (I4.0) in the automotive industry, exploring its contribution to a shift in the maintenance paradigm. To this end, we firstly present the concepts of predictive maintenance (PdM), condition-based maintenance (CBM), and their applications to increase awareness of why and how these concepts are revolutionizing the automotive industry. Then, we introduce the business process management (BPM) and business process model and notation (BPMN) methodologies, as well as their relationship with maintenance. Finally, we present the case study of the Renault Cacia, which is developing and implementing the concepts mentioned above.
... Kong and Frangopol, 2004;Speijker et al 2000). The result has been a steady increase in risk levels, both for infrastructures (Al-Najjar, 2007) and coastal habitats (Coanda et al., 2020), compounded by a lack of bespoke predictive maintenance (Hermans et al., 2013). A predictive maintenance programme, linking observations and inspections with numerical and/or experimental analyses of water-sediment-structure-ecosystem interactions, specially under extreme conditions, would reduce coastal risks during the life cycle of coastal structures or habitats (Yang et al 2004;Yang et al 2006;Okasha and Frangopol, 2009). ...
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Klima Aldaketa eta Itsasertzari buruzko Mugaz gaindiko V. Biltzarra. Laburpen zabalak V Congreso transfronterizo sobre Cambio Climático y Litoral. Abstracts extendidos V Congrès transfrontalier sur le Changement Climatique et Littoral. Abstract étendue V Cross border conference on climate and coastal change. Extended abstracts
... Kong and Frangopol, 2004;Speijker et al 2000). The result has been a steady increase in risk levels, both for infrastructures (Al-Najjar, 2007) and coastal habitats (Coanda et al., 2020), compounded by a lack of bespoke predictive maintenance (Hermans et al., 2013). A predictive maintenance programme, linking observations and inspections with numerical and/or experimental analyses of water-sediment-structure-ecosystem interactions, specially under extreme conditions, would reduce coastal risks during the life cycle of coastal structures or habitats (Yang et al 2004;Yang et al 2006;Okasha and Frangopol, 2009). ...
Full-text available
Klima Aldaketa eta Itsasertzari buruzko Mugaz gaindiko V. Biltzarra. Laburpen zabalak V Congreso transfronterizo sobre Cambio Climático y Litoral. Abstracts extendidos V Congrès transfrontalier sur le Changement Climatique et Littoral. Abstract étendue V Cross border conference on climate and coastal change. Extended abstracts
... However, in a general division three different strategies can be distinguished: 1. reactive maintenance, 2. preventive maintenance and 3. predictive maintenance. Reactive maintenance and preventive maintenance are two main maintenance strategies defined in the standard EN 13306 [30,31]. In turn, predictive maintenance as a relatively new concept is developing as an alternative for preventive maintenance in order to ensure the increasing requirements of reliability, availability, maintainability and safety of systems with the use of testing techniques to monitor and evaluate equipment performance trends [32]. ...
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In this paper, an assessment of the failure frequency of machines in a series-parallel structure was conducted. The analyses contain the decomposition of the system according to the general theory of complex systems. Based on the results obtained, a model for an optimal determination of the mean time to failure (MTTF) according to the expected value of the gamma distribution was proposed. For this purpose, the method of moments was used to determine the optimal values of the parameters of the estimated gamma distribution. The article is designed to be analytical. The object of consideration in this analysis is the real production system working in accordance with make-to-order, with a high degree of product customisation. Moreover, in the considered system occurs a dichotomy of mutually exclusive flows: push and pull. In the article, the main emphasis was placed on the applicability of the proposed MTTF value-shaping algorithm. Then, the maintenance strategy for each machine (reactive, preventive or predictive) was proposed. Maintenance strategy selection considered sustainable development principles in the criterion of minimizing maintenance actions, fulfilling the assumption of not interrupting the flow of the processed material. Based on inductive analyses, the concepts of improvement actions individually for each machine in the analysed subsystem were deductively defined. As a result, it was proved that a reactive maintenance strategy is appropriate for machines that have manufacturing reserves and are low priority. The equipment possessing manufacturing reserves but also having an impact on the risk of interrupting the flow of the processed material should be operated in accordance with a preventive maintenance strategy. A predictive maintenance strategy was proposed for the machines with the highest priority, which simultaneously do not have manufacturing reserves and the risk of manufacturing line operation interruption is high. The considerations were conducted with a holistic approach, taking into account the main functional areas of the enterprise.
Over the years, more and more industries are focused on digitizing their manufacturing operations by using a bunch of advanced technologies like Machine Learning and Artificial Intelligence based on different equipments and materials, such as sensors, cameras, and lidar. All of them could be combined to wireless technology communication and create an IoT network. In this context, the objective is to present our contribution in the field of failure prediction in Rotation machinery based on diagnosis and prognosis system for predictive maintenance. With the help of the new intelligent diagnostic indicators, it is possible to target default points in real time before taking actions using the stream processing. Machine analysis behavior is a traditional approach used in maintenance field to capture damage and failure. It is also a perfect tool for detecting and then diagnosing operating default in rotating machines.The present work is about predicting the situation using a new Wireless sensor Network in rotating machinery by capturing and treating all the collected data and testing them with Machine Learning algorithm.KeywordsPredictive MaintenanceWireless Sensors NetworkRotating MachineryVibration analysis
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Highly secured forensic document examiners are devices of great demand with the advancement of artificial intelligence and processing power. In most cases, it can be seen that there exists a rush from law enforcement agencies and criminals to utilize new methods for the discovery of fraudulent acts or for the achievement of a perfect fraudulent act. This work aims to extend the abilities of the forensic document examiner device Fordex by proposing the use of Blockchain technology to eliminate trust issues in the field of forensics. Fordex is a device that is currently used in forensic document analysis. It is developed by TÜBİTAK, BİLGEM, UEKAE, and Bioelectronics Systems Laboratory. It is intended to use Hyperledger Fabric, a permission Blockchain platform, in the Blockchain environment. In the proposed system, the Fordex software and the Fordex-Forensic-Chain (FFC) Blockchain system will interact within the Hyperledger Fabric platform in a reliable and scalable manner. The proposed architecture allows the system administrator to access and examines records of case studies tested by the Fordex device. The designed control mechanism protects the forensic images using the SHA256 hash algorithm while keeping them in the traditional database and alerts the system administrator in case of any unauthorized change in the recorded data. To the best of our knowledge, the FFC will be the first Blockchain application in which forensic devices are used.
The IoT sector leads improvements in the Industry 4.0 revolution. Failure-prone machinery puts operations and production costs at risk. A sudden failure results in high downtime expenses and a drop in output. Predictive maintenance is a crucial area to research in order to increase industrial productivity. The purpose of the paper is to present the proper method for implementing predictive maintenance using the soft voting undersampling approach. We also highlight the impact of ensemble learning on an imbalanced dataset that is crucial problem for modelling the failure-prone machinery dataset. This paper suggests a method for enhancing predictive learning by selecting six different machine learning algorithms, including decision tree, random forest, Gradient Boosting Machines (GBM), XGBoost, Light GBM, and CatBoost classifiers. The soft voted model is proposed to enhance the performance of machine learning classifiers, which have produced better results in terms of the Fowlkes-Mallows Index (FMI) and Cohen's Kappa score. The results of our study are close to the results of predictive maintenance studies in the literature.
In order to ensure as continuous as possible nuclear energy production, it is necessary to guarantee the availability of all equipment involved in the production chain, in all its complexity. The safety of all these equipments is based on a good command of the maintenance policy with very specific conditions related to the demanding regulations of the nuclear field. It is about having facilities that are safe, available with a good quality and a limitation to radiological risk.Today, with the advancement of digital technology, substantial improvements have occurred in the tools that can be applied in the maintenance and monitoring of Structures, Systems and Components (SSCs), enabling an understanding of equipment performance far beyond that available only a few decades ago. Therefore, predictive maintenance becomes a subject of prior interest for the nuclear industry.In this paper, we will emphasize the incentives and obstacles of predictive maintenance deployment in a nuclear context. The objectives here are to draw a clear picture of what can be the practices of predictive maintenance in the nuclear industry context and to identify the requirements and the needs to implement a predictive maintenance model on the lifecycle management of a nuclear facility.KeywordsLifecycle managementPredictive maintenanceNuclear system
The paper presents some technical features regarding the realization of dental prostheses and models by modern CAD-CAM technologies that operate on both the subtractive principle and the additive principle highlighting the advantages and disadvantages for each manufacturing approach compared to traditional methods applied in dental prosthetics. It also describes the processing steps for making dental prostheses by CAD-CAM methods, the equipment used, as well as the characteristics of the resulting restorations.KeywordsAdditive manufacturingDental prosthesesCAD-CAM dental milling
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Fault diagnosis and prognosis in mechanical systems have been researched and developed in the last few decades at a very rapid rate. However, owing to the high complexity of machine centers, research on improving the accuracy and reliability of fault diagnosis and prognosis via data mining remains a prominent issue in this field. This study investigates fault diagnosis and prognosis in machine centers based on data mining approaches to formulate a systematic approach and obtain knowledge for predictive maintenance in Industry 4.0 era. We introduce a system framework based on Industry 4.0 concepts, which includes the process of fault analysis and treatment for predictive maintenance in machine centers. The framework includes five modules: sensor selection and data acquisition module, data preprocessing module, data mining module, decision support module, and maintenance implementation module. Furthermore, a case study is presented to illustrate the application of the data mining methods for fault diagnosis and prognosis in machine centers as an Industry 4.0 scenario.
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A variety of reasons, specifically contact issues, irregular loads, cracks in insulation, defective relays, terminal junctions and other similar issues, increase the internal temperature of electrical instruments. This results in unexpected disturbances and potential damage to power equipment. Therefore, the initial prevention measures of thermal anomalies in electrical tools are essential to prevent power-equipment failure. In this article, we address this initial prevention mechanism for power substations using a computer-vision approach by taking advantage of infrared thermal images. The thermal images are taken through infrared cameras without disturbing the working operations of power substations. Thus, this article augments the non-destructive approach to defect analysis in electrical power equipment using computer vision and machine learning. We use a total of 150 thermal pictures of different electrical equipment in 10 different substations in operating conditions, using 300 different hotspots. Our approach uses multi-layered perceptron (MLP) to classify the thermal conditions of components of power substations into -defect- and -non-defect- classes. A total of eleven features, which are first-order and second-order statistical features, are calculated from the thermal sample images. The performance of MLP shows initial accuracy of 79.78%. We further augment the MLP with graph cut to increase accuracy to 84%. We argue that with the successful development and deployment of this new system, the Technology Department of Chongqing can arrange the recommended actions and thus save cost in repair and outages. This can play an important role in the quick and reliable inspection to potentially prevent power substation equipment from failure, which will save the whole system from breakdown. The increased 84% accuracy with the integration of the graph cut shows the efficacy of the proposed defect analysis approach.
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Since continuous real-time components or equipment condition monitoring is not available for injection molding machines, we propose a predictive maintenance approach that uses injection molding process parameters instead of machine components to evaluate the condition of equipment. In the proposed approach, maintenance decisions are made based on the statistical process control technique with real-time data monitoring of injection molding process parameters. First, machine components or equipment of injection molding machines, which require maintenance, is identified and then injection molding process parameters, which may be affected by malfunctioning of the previously identified components, are identified. Second, regression analysis is performed to select the process parameters that significantly affect the quality of the lens and require a high degree of attention. By analyzing the patterns of real-time monitored data series of process parameters, we can diagnose the status of the components or equipment because the process parameters are affected by machine components or equipment. Third, statistical predictive models for the selected process parameters are developed to apply statistical analysis techniques to the monitored data series of parameters, in order to identify abnormal trends. Fourth, when abnormal trends or patterns are found based on statistical process control techniques, maintenance information for related components or equipment is notified to maintenance workers. Finally, a prototype system is developed to show feasibility in a LabVIEW® environment and an experiment is performed to validate the proposed approach.
Conference Paper
In the current manufacturing world, the role of maintenance has been receiving increasingly more attention while companies understand that maintenance, when well performed, can be a strategic factor to achieve the corporate goals. The latest trends of maintenance leans towards the predictive approach, exemplified by the Prognosis and Health Management (PHM) and the Condition-based Maintenance (CBM) techniques. The implementation of such approaches demands a well structured architecture and can be boosted through the use of emergent ICT technologies, namely Internet of Things (IoT), cloud computing, advanced data analytics and augmented reality. Therefore, this paper describes the architecture of an intelligent and predictive maintenance system, aligned with Industry 4.0 principles, that considers advanced and online analysis of the collected data for the earlier detection of the occurrence of possible machine failures, and supports technicians during the maintenance interventions by providing a guided intelligent decision support.
This second volume moves beyond a general introduction to product lifecycle management (PLM) and its principal elements to provide a more in-depth analysis of the subjects introduced in Volume 1 (21st Century Paradigm for Product Realisation). Providing insights into the emergence of PLM and the opportunities it offers, key concepts such as the PLM Grid and the PLM Paradigm are introduced along with the main components of PLM and the associated characteristics, issues and approaches. Detailing the 10 components of PLM: objectives and metrics; management and organisation; business processes; people; product data; PDM systems; other PLM applications; facilities and equipment; methods; and products, it provides examples and best practices. The book concludes with instructions to help readers implement and use PLM successfully, including outlining the phases of a PLM Initiative: development of PLM vision and strategy; documentation of the current situation; description of future scenarios; development of implementation strategies and plans; implementation and use. The main activities, tasks, methods, timing and tools of the different phases are also described.
The introduction into this thesis gives a brief description on the underlying research problem, the objective of the thesis, the research questions and method as well as a summary of the thesis outline.
Christoph Jan Bartodziej examines by means of an empirical study which potential Industry 4.0 technologies do have regarding end-to-end digital integration in production logistics based on their functions. According to the relevance of the concept Industry 4.0 and its early stage of implementation it is essential to clarify terminology, explain relations and identify drivers and challenges for an appropriate use of Industry 4.0 technologies. The results will constitute a profound basis to formulate recommendations for action for technology suppliers and technology users. Contents Theoretical Background on Technologies The Concept Industry 4.0 Technologies and Functions Within the Concept Industry 4.0 Empirical Study to Identify Technology Potential Technology Potential and Recommendations for Action Target Groups Researchers and students in the fields of economic science and political economy Executives and politicians in these areas The Author Christoph Jan Bartodziej graduated from Technical University of Berlin with a Master of Science in Industrial Engineering and Management under the supervision of Prof. Dr.-Ing. Frank Straube at the logistics chair. He is currently engaged as management consultant for technology related advisory and implementation topics in an international consultancy company.
Conference Paper
Maintenance is the most expensive aspect in the sustainment of a system. All maintenance approaches can be classified under two basic approaches: reactive and proactive maintenance. Which one provides the greatest benefit to the organization? Neither plan will completely eliminate downtime, but proactive maintenance will help minimize it. When management sees the large initial investment required to implement a proactive maintenance plan, it often deters them from making a change because they are concerned with the current year's bottom line. However, a large initial investment is required to implement a successful proactive maintenance plan due to strategic planning and equipment needed to detect condition indicators. Reactive maintenance does not have the large upfront costs, because it does not require a system to monitor the failure modes and mechanisms acting upon the part. Reactive maintenance restores system functionality, while proactive maintenance preserves system functionality. Proactive maintenance can be called many different names. One such proactive maintenance approach is predictive maintenance. It uses statistics to determine the optimal time interval to replace parts to prevent the system from going into a down state. This statistics based approach works for equipment that have well-defined operating conditions so time can be correlated with part failure and is the Time Directed Maintenance (TDM) path of Reliability Centered Maintenance (RCM) [1]. RCM can also lead to the implementation of Condition Based Maintenance (CBM), where condition indicators are used to measure an indicator of the consumed life of a component. To be considered proactive maintenance, one must take measures to be being proactive with how maintenance is being utilized. Proactive and reactive maintenance plans experience the same costs for spares, labor rates for employees, and overhead. The plans begin to differ with the amount of time required to perform the maintenance tasks. A reactive maintenance plan must wait its turn for the maintenance action to be performed. When the system is not in operations the organization incurs lost opportunity costs. Proactive maintenance minimizes the amount of overall downtime a system incurs for a maintenance event. This is due to scheduling of maintenance events to ensure that the right people, tools, facilities and parts are available to quickly perform the maintenance action. The reactive maintenance approach waits for system failure before addressing the issue. This method incurs lengthy downtime because nothing is scheduled causing one to wait until the correct people, parts, tools and facilities become available to perform the repairs. Maximizing profits is key to the cash flow of an organization. When comparing the two alternatives the lost opportunity cost must be included in the cost analysis to get a better understanding of the different approaches to maintenance. This allows management and engineers to determine the optimal maintenance plan to implement for their system. The costs for proactive maintenance are expensive, but the savings it provides makes it a worthwhile investment much sooner if lost opportunity is included in the analysis.