Availability Analysis of Gas Turbines Used in Power Plants

Abstract

The availability of a complex system, such as a gas turbine, is strongly associated with its parts reliability and maintenance policy. That policy not only has influence on the parts’ repair time but also on the parts’ reliability affecting the system degradation and availability. This study presents a method for reliability and availability evaluation of gas turbines installed in an electric power station. The method is based on system reliability concepts, such as functional tree development, application of failure mode and effects analysis to identify critical components for improvement of system reliability, and reliability and maintainability evaluation based on a historical failure database. The method also proposes the application of Reliability Centered Maintenance concepts to improve complex system maintenance policies aimed at the reduction of unexpected failure occurrences in critical components. The method is applied to the analysis of two F series gas turbines, each with an output of 150 MW, installed in a 500 MW combined cycle power plant. The reliability and availability of the turbines are simulated based on a five-year failure database. The availability analysis shows different results for each turbine, one presenting 99% and the other 96% availability, indicating differences in their systems installation and operation.

Full text

28 / Vol. 12 (No. 1) * Corresponding Author
Int. J. of Thermodynamics Vol. 12 (No. 1), pp. 28-37, March 2009
ISSN 1301-9724 www.icatweb.org/journal.htm
Availability Analysis of Gas Turbines Used in Power Plants
Fernando Jesus Guevara Carazas and Gilberto Francisco Martha de Souza1*
Polytechnic School, University of São Paulo, São Paulo,
Av. Prof. Mello Moraes, 2231 – Cidade Universitária – São Paulo – SP –Brazil
E-mail: 1gfmsouza@usp.br
Abstract
The availability of a complex system, such as a gas turbine, is strongly associated with its parts reliability and
maintenance policy. That policy not only has influence on the parts’ repair time but also on the parts’ reliability
affecting the system degradation and availability. This study presents a method for reliability and availability
evaluation of gas turbines installed in an electric power station. The method is based on system reliability concepts,
such as functional tree development, application of failure mode and effects analysis to identify critical components
for improvement of system reliability, and reliability and maintainability evaluation based on a historical failure
database. The method also proposes the application of Reliability Centered Maintenance concepts to improve
complex system maintenance policies aimed at the reduction of unexpected failure occurrences in critical
components. The method is applied to the analysis of two F series gas turbines, each with an output of 150 MW,
installed in a 500 MW combined cycle power plant. The reliability and availability of the turbines are simulated
based on a five-year failure database. The availability analysis shows different results for each turbine, one
presenting 99% and the other 96% availability, indicating differences in their systems installation and operation.
Keywords: Availability, gas turbines, maintainability, RCM and reliability simulation.
1. Introduction
The use of combined cycle power stations has increased
in recent years due mainly to the development of high
nominal output gas turbines. The high temperature exhaust
gas from the Brayton cycle is used as a heat source for the
Rankine cycle, which increases the power plant global
efficiency.
Reliable gas turbine operation can be considered critical
for the combined cycle operation. The gas turbine
transforms the thermal energy generated by fuel
combustion into mechanical energy to rotate the electrical
generator’s shaft. The exhaust gas is used to produce steam
in a steam generator that is part of the steam cycle. In case
of a gas turbine failure the power plant is fully shut down
since the Rankine cycle is dependent on the gas turbine
availability. Bearing in mind the great importance of the
gas turbine for plant operation, its availability should be
carefully evaluated in order to anticipate the performance –
technical and economical - of the plant.
Availability measures are concerned with the fraction of
time a unit is capable of providing service. Most power
plants use the index proposed by IEEE std. 762 (1987) to
define availability. That index represents the percentage of
a given period of time, expressed in hours, that the unit is in
service (including reserve shutdown state). A reduction in
availability is caused by planned maintenance and
unplanned maintenance actions. The index, usually
evaluated monthly, is reported in a Generating Availability
Data System (GADS) and can be used for comparison
between different generating systems.
That index is deterministic and can only be used for
maintenance efficiency management. In order to improve
maintenance efficiency and to reduce maintenance costs,
Eti et al. (2007) proposed the use of reliability and
maintainability concepts to define an availability index
expressed by the ratio of the mean time to failure to the sum
of the mean time to failure plus the mean time to repair.
Those authors indicate that the mean time to failure,
calculated from the failure records, can be improved
through the study of root-cause failure analysis and system
reliability analysis.
The availability of a complex system, such as a gas
turbine, is strongly associated with the parts reliability and
the maintenance policy. That policy not only influences the
parts’ repair time but also the parts’ reliability affecting the
system degradation and availability.
Most of the maintenance tasks of power plant
equipment are based on manufacturer’s recommendations.
Those recommendations are not always based on real
experience data. Many manufacturers get very little
feedback from users of their equipment after the guarantee
period is over. Fear of product liability claims may perhaps
also influence the manufactures’ recommendations.
In a large enterprise, such as a power plant, keeping
asset reliability and availability, and reducing costs related
to asset maintenance, repair, and ultimate replacement are
at the top of management concerns. In response to these
concerns, the Reliability Centered Maintenance (RCM)
concept was developed. RCM has been formally defined by
Moubray (1997) as “a process used to determine what must
be done to ensure that any physical asset continues to do
whatever its users want it to do in its present operating
context”.
The RCM methodology is completely described by the
following four features:
i) Preserve functions;
ii) Identify failure modes that can defeat the functions;
iii) Prioritize function need;

Int. J. of Thermodynamics (IJoT) Vol. 12 (No. 1) / 29
iv) Select main monitoring systems to evaluate critical
component degradation to allow the definition of
maintenance actions before the occurrence of
functional failure.
This paper presents a system reliability-based method
used to identify the most critical components in a gas
turbine. The criticality is associated with the component
failure effect on the turbine operation condition. The higher
the criticality of the component, the more technical and
financial resources should be expended in the maintenance
activities to keep the gas turbine available for operation.
The RCM concepts are used as a guideline for ranking the
maintenance policy priorities for the critical components
aiming at the overall gas turbine operation availability.
2. Method Development
The method is based on system reliability concepts.
The method’s first step consists in the elaboration of the
turbine functional tree that allows the definition of the
functional links between the equipment subsystems.
Although all gas turbines possess essentially the same
subsystems, such as compressor, combustion chamber and
turbine, there are differences between the technologies used
by the manufacturers, therefore the functional tree must be
developed for each specific gas turbine model.
The next step is the development of the Failure Mode
and Effects Analysis (FMEA) of each turbine component in
order to define the most critical components for turbine
operation. FMEA provides a lot of valuable qualitative
information about the system design and operation, since its
goal is to identify, concisely, the failure modes and
mechanism of interest. The quantitative treatment of
failures will be carried out in the analysis’ third step, using
reliability concepts instead of FMECA approach (where the
“C” stands for “criticality”).
The analysis is based on the evaluation of the
component failure effect on the turbine operation (Lewis,
1987). For the definition of the system degradation, the
FMEA analysis uses a numerical code, usually ranking
from 1 to 10. The higher the number the higher is the
criticality of the component that must be evaluated for each
component failure mode. For the present analysis that index
is classified into three main severity levels: marginal,
critical and catastrophic. Each level is split into three other
sub-levels to express some variety of failure effects. A
criticality scale between 1 and 9 is proposed. Values
between 1 and 3 express minor effects on the turbine
operation while values between 4 and 6 express significant
effects on the turbine operation. Finally, failures that cause
turbine unavailability or environmental degradation are
classified by criticality values between 7 and 9. The
description of the effects associated with the highest
criticality index is presented in Table 1.
The method’s third step involves a reliability analysis
based on the ‘time to failure’ data recorded during the gas
turbine operation. The failures should be classified
according to the subsystem presented on the functional tree.
The reliability of each subsystem is calculated based on the
failure data and the gas turbine reliability is simulated
through the use of a block diagram. Considering the ‘time
to repair’ data and the preventive maintenance tasks
associated with the equipment, the gas turbine availability
is evaluated using the block diagram.
Once the critical components are defined a maintenance
policy can be proposed for those components considering
the RCM concepts.
This maintenance policy philosophy is focused on the
use of predictive or preventive maintenance tasks that aim
at the reduction of unexpected failures during the
component’s normal operation (Smith and Hinchcliffe,
2004).
For complex systems, such as gas turbines, the
occurrence of unexpected component failures drastically
increases maintenance costs associated with corrective
tasks not only for the direct corrective costs (spare parts,
labor hours) but also for the system unavailability costs.
The reliability block diagram analysis allows the
prediction of a possible availability improvement
considering the application of new maintenance procedures,
expressed by the reduction of corrective maintenance repair
time.
In Figure 1 a flowchart is used to illustrate the method
main steps.
Table 1. Criticality Index Description for FMEA Analysis.
Criticality Index Effects on the Turbine Operation
7
(Severe)
This severity ranking is given when a component potential failure mode can cause unavailability of the equipment
but does not cause damage to other equipment components, possibly affecting:
i) the equipment operation, since it must be stopped; ii) the environmental in a severe manner; iii) the compliance
with government requirements.
The failure also causes the need for repair and/or replacement of the failed component. The plant is unavailable
for a short period of time.
8
(Very Severe)
This severity ranking is given when a component potential failure mode can cause unavailability of the equipment
but does not cause damage to other equipment components, possibly affecting:
i) the equipment operation, since it must be stopped; ii) the environmental in a very severe manner; iii)
compliance with government requirements.
The failure also causes the need for repair and/or replacement of the failed component. The plant is unavailable
for a long period of time.
9
(Hazardous
Effects)
This severity ranking is given when a component potential failure mode can cause severe damage to other
components and/or to the equipment, possibly affecting:
i) the equipment operation, since it must be stopped; ii) the environmental safety, including leakage of hazardous
materials; iii) the safe power plant operation; iv) the compliance with government requirements.
The failure also causes the need for repair and/or replacement of a great number of components. The plant is
unavailable for long period of time.

30 / Vol. 12 (No. 1) Int. Centre for Applied Thermodynamics (ICAT)
Figure 1. Flowchart for Complex System Availability
Evaluation.
3. Application
The method is applied to the analysis of two identical
heavy-duty F series gas turbines, with a 150 MW nominal
power output, installed in a 500 MW combined cycle power
plant located in Brazil. The reliability and availability of the
turbines are simulated based on a five-year failure database.
3.1 Functional Tree
The functional tree for the power plant is presented in
Figure 2 and was divided into seven main systems: gas
turbine, steam turbine, and electrical generator, heat
recovery steam generator, cooling water system, water
treatment system and electrical station.
The functional tree for the gas turbine is
presented in Figure 3. The equipment is divided into six
main subsystems: trunnion, air inlet, compressor,
combustion, turbine and turning gear (start/stop subsystem).
Those subsystems are divided into components, each one
performing a specific function in connection with the
subsystem main function. A failure in a component at the
bottom of the tree affects all subsystems above it, causing a
possible degradation in the turbine operation, represented
by any reduction in the nominal power output or even
environmental degradation. The tree was developed
according to the operation manual furnished by the
manufacturer.
3.2 Failure Mode and Effects Analysis
Although there are many variants of FMEA, it is always
based on a table, as shown in Figure 4. In the left-hand
column the component under analysis is listed; then in the
next column the physical modes by which the component
may fail are provided. This is followed, in the third column,
by the possible causes of each of the failure modes.
The fourth column lists the effects of each failure mode
that are classified according to the criticality scale, which
expresses the degradation degree in the turbine operation.
The FMEA analysis was performed for each component
listed in the end of a given branch of the functional tree. In
Figure 4 the analysis for the trunnion support is partially
presented as an example.
The failure modes for the components were developed
according to manufacturer’s information and other failure
analysis available in the open literature, (Black & Veatch,
1996), (Park et al., 2002), (Baumik, 2002), (Burgazzi,
2004), (Chang et al., 2003), and (Guan, 2005).
The analysis pointed out that the most critical
components for the turbine are:
- compressor: blades, vanes and shaft;
- bearings: oil heat exchanger, pump, piping and filter;
- combustion subsystem: combustion chamber, ignition
and transition duct;
- turbine: blades and vanes refrigeration system, shaft
and exhaustion;
- turning gear: gear box.
These systems are regularly submitted, according to
manufacturer recommendation, to a detailed maintenance
policy based on predictive or preventive techniques,
including annual inspections.
3.3 Maintenance Tasks Recommendations
The maintenance policy of gas turbines is typically
based on five or six year cycles. During the first two years
some annually based basic preventive tasks are performed.
In the middle of the cycle a more complex inspection is
performed. After that the basic tasks are performed
annually and at the end of the cycle overhaul maintenance
is performed.
Based on the results of the FMEA analysis, the RCM
concepts can be used to recommend maintenance tasks to
those components that have a criticality index greater than
6. The failure of those components can cause severe
degradation in the gas turbine’s performance, significantly
reducing the power output of the generator coupled to the
turbine shaft which affects the gas turbine main function.
The gas turbine presently analyzed has a complex
monitoring system based on temperature, pressure and
vibration gauges. That system can be used to monitor the
real-time performance of the critical components of the gas
turbine allowing the use of condition-based maintenance
policy to improve the equipment availability. Those data
can be used to define the trend in the equipment
performance and a limit value must be selected as a
potential failure indicator.
Figure 2. Combined –Cycle Power Plant Functional Tree.

Int. J. of Thermodynamics (IJoT) Vol. 12 (No. 1) / 31
Figure 3. Gas Turbine Functional Tree.

32 / Vol. 12 (No. 1) Int. Centre for Applied Thermodynamics (ICAT)
Function Failure Mode Failure Causes Failure Effects Criticality
Support
Turbine
housing
Achieve Ultimate limit
state
Fatigue failure, fracture, Buckling Loss of structural support,
extensive damage to the turbine
9
Achieve operational
limit state
Plastic deformation due to
overloading, existence of fatigue
crack
Loss of structural stiffness,
possible turbine vibration.
8
Figure 4. Failure Mode and Effects Analysis: Example – Trunnion Support.
That value allows identification of the alert level,
providing information to schedule maintenance tasks before
functional failure occurs. The analysis is the basis for the
implementation of the predictive maintenance policy
recommended by RCM philosophy. Moreover, most of the
critical components of the gas turbine can be assisted with
predictive maintenance tasks although, according to
manufactures’ recommendation, other critical components
are assisted with preventive maintenance tasks.
For some critical components the authors detected that
there is no monitoring system that could indicate the
evolution of a given failure mode. For those components
the authors suggest the following measures aiming at the
reduction of their failure probability:
i) The pressure and temperature magnitude in the
combustion chamber are monitored allowing the on-line
diagnosis of the combustion process. As a consequence,
corrections in the turbine operational conditions can be
rapidly implemented;
ii) Regarding the sliding bearing systems (radial and
thrust), the lubrication oil pressure and temperature are
monitored at the bearing inlet. Based on the trends of the
sensors registers the lube oil system operational condition
can be monitored but not the bearing material degradation.
For that analysis the authors recommend that periodically
an oil sample should be analyzed in order to detect the
presence of metallic particles in the fluid. The time history
of those particles’ volume and chemical composition can be
used as indicators of bearing material degradation allowing
the planning of maintenance actions before the bearing
failure;
iii) Only the thrust bearing has an axial vibration
monitoring system. Although the trend in the thrust bearing
vibration pattern can be used to characterize a potential
failure development in the sliding bearings, the monitoring
system could be improved through the use of vibration
sensors in the radial sliding bearing. With that improvement
more information about the rotating parts of the gas turbine
should be recorded supporting preventive or predictive
maintenance tasks planning;
iv) The filtering systems installed in the air inlet and in
the lubrication oil circuit are monitored through the use of
differential pressure gauges. Any change in the differential
pressure trend can be used as an indication of the presence
of solid particles in the filter cartridge. The authors
recommend that the solid particles removed during the
cartridge cleaning operation could be analyzed to check if
there are metallic materials in the particles that can be used
as an indication of turbine parts deterioration;
v) The sliding and thrust bearings lubrication oil pumps
do not have any monitoring system. As any degradation in
those pumps’ performance can affect the bearings
operational condition that could even cause the turbine to
trip, the authors recommend the use of non-destructive
techniques to check their performance. The thermograph
technique can be used to check the pump electric motor
operational condition and a vibration monitoring system
can be used to check the pump performance. The trends in
those registers can be used to support on condition
maintenance tasks planning avoiding any degradation in the
bearings performance;
vi) The lubrication oil refrigeration system must be also
considered in the predictive maintenance planning since its
performance degradation has consequences on the turbine
bearings operational condition. The cooling water pumps
should be monitored with vibration gauges and some flow
meters can be used to check the water flow in the oil /water
heat exchange system.
The use of monitoring systems allows the evaluation of
failure development only for mechanical components.
Nevertheless, to allow any trend evaluation, one needs to
define the signature pattern representing the component
operational performance in the normal condition defined in
the design specifications. A recorded signature pattern
associated with each monitored component is compared to
the trends presented by the signal registered by the sensors
allowing the maintenance tasks planning before the
equipment functional failure.
3.4 Reliability and Availability Analysis
Reliability can be defined as the probability that a
system will perform properly for a specified period of time
under a given set of operating conditions. Implied in this
definition is a clear-cut criterion for failure, from which one
may judge at what point the system is no longer functioning
properly. For the gas turbine the failure criterion is any
component failure that causes incapacity of generating the
nominal power output.
The reliability analysis is performed for each of the two
gas turbines installed in the power plant, submitted to the
same commissioning process and starting to operate at the
same time. The reliability analysis is based on the time to
failure data analysis.
Probably the single most used parameter to characterize
reliability is the mean time to failure (MTTF). It is just the
expected or mean value of the failure time, expressed as:
()
∫
∞
=
0
dttRMTTF (1)
where:
R(t) reliability at time t
T time period [h]
Random failures (represented by the exponential
probability function) constitute the most widely used model
for describing reliability phenomena. They are defined by
the assumption that the rate of failure of a system is
independent of its age and other characteristics of its
operating history. In that case the use of mean time to
failure to describe reliability can be acceptable once the

Int. J. of Thermodynamics (IJoT) Vol. 12 (No. 1) / 33
exponential distribution parameter, the failure rate, is
directly associated with MTTF.
The constant failure rate approximation is often quite
adequate even though a system or some of its components
may exhibit moderate early-failures or aging effects. The
magnitude of early failures is limited by strict quality
control in manufacturing and aging effects can be sharply
limited by careful predictive or preventive maintenance.
In the beginning of the operational life of complex
equipment such as a gas turbine, the presented failure
modes are not usually random and cannot be represented by
an exponential reliability distribution. The equipment's
initial performance depends on commissioning and
operational procedures and even on environmental
conditions that can induce the occurrence of early failure
modes.
When the phenomena of early failures, aging effects, or
both, are presented, the reliability of a device or system
becomes a strong function of its age.
The Weibull probability distribution is one of the most
widely used distributions in reliability calculations
involving time related failures. Through the appropriate
choice of parameters a variety of failure rate behaviors can
be modeled, including constant failure rate, in addition to
failure rates modeling both wear-in and wear-out
phenomena.
The turbine is modeled as one block. For that block the
reliability and maintainability distributions are estimated
based on failure reports presented by the plant operator.
The two-parameter Weibull distribution, typically used
to model wear-out or fatigue failures is represented by the
following equation:
β
η
⎟
⎟
⎠
⎞
⎜
⎜
⎝
⎛
−
=
t
etR )( (2)
where:
R(t) reliability at time t
t time period [h]
β
Weibull distribution shape parameter
η
Weibull distribution characteristic life [h]
The distribution parameters are estimated through the
use of parametric estimation methods that fit the
distribution to the ‘time to failure’ data. There are
procedures for estimating the Weibull distribution
parameters from data, using what is known as the
maximum likelihood estimation method. For the gas turbine
reliability analysis the software Weibull++ (Reliasoft,
2003) was used for parameter estimation.
Table 2 shows the Weibull distribution parameters for
the two gas turbines.
Table 2. Weibull Distribution Parameters for Turbine
Reliability Calculations
System Parameters
Gas Turbine 1
β
=0,58
η
=1014,56
Gas Turbine 2
β
=0,44
η
=497,24
Both gas turbines have reliability distributions with
shape parameters less than one. When 0 < β < 1, the
distribution has a decreasing failure rate.
Turbine 1 presented 15 failures that caused equipment
unavailability in the analysis period. Several of those 11
failures occurred in the first two operational years. Most of
them were related to high temperature in the combustors or
excessive vibration on the bearings. The failure root-cause
was sensor calibration problems. In the last three years
there were two failures due to high temperature in the
exhaust collector caused by combustor failure.
Turbine 2 presented 24 failures in five operational years
and 12 of them occurred in the first two years. Three of
those failures were related to calibration problems of
pressure gauges located at the exhaust collector and the
other three failures were related to fuel filters premature
cleaning due to premature clogging caused by poor natural
gas quality. In the last three years, the main problems were
related to the lubrication oil system, mainly the oil feeding
pressure.
The failures that may affect turbine availability were
associated with components listed at the bottom of
functional tree branches presented in Figure 3 and were
considered as critical in the FMEA analysis.
For the gas turbines, the early failure stage, defined by
the large failure concentration in the first two operational
years, is mainly associated with the adjustment of control
systems.
The shape parameter of the turbine 1 reliability function
is higher than the same parameter estimated for turbine 2.
This fact indicates that the first turbine is getting close to
the period of random failures characterized by a shape
parameter equal to one.
The reliability distribution curve for turbines 1 and 2 are
presented in Figure 5. The points presented in the graphic
represent the median rank plotting reliability estimate for
each of the time to failure data, arranged in increasing
order. Those points are used to verify the adherence of the
reliability distribution to the failure data.
Figure 5. Turbines 1 and 2 Reliability Distribution.
For complex systems, the concern about reliability starts
in the early design stages to achieve the required functional
requirements. The manufacturer defines the basic
characteristics in which the system is to function such as
the range of air temperature and humidity, the concentration
of dust or other contaminants in the air as well as the
quality of fuel.
From such requirements a conceptual design is
formulated that in broad form outlines how the system is to
function and provides a general plan for its construction.
The conceptual design must be converted into a detailed set
of drawings and specifications from which the system can

34 / Vol. 12 (No. 1) Int. Centre for Applied Thermodynamics (ICAT)
be built and maintenance requirements and procedures are
also likely to take shape in a detailed form. In the later
stages of the design process, prototypes are built and the
reliability tests may be performed. In the design of
equipment for power plants, such as gas turbines, the data
gained from subsystem prototype reliability tests provide a
valuable understanding of failure modes and may suggest
refinements that would increase overall system reliability.
Unfortunately, usually the gas turbines manufacturers
do not perform detailed end product reliability analysis due
to test program cost and difficulties to simulate in a
laboratory environment the operational requirements of a
gas turbine.
So the equipment reliability estimative is usually based
on system reliability analysis methods, including
information on subsystem reliability and failure data
collected in the field during regular operation of similar gas
turbine models.
The subsystem parts manufacturing process is
monitored and controlled by the methods of statistical
quality control in order to eliminate problems in
manufacturing that could affect parts, and consequently,
system reliability.
Although the design process and subsystem parts
manufacture planning are based on reliability concepts
aiming to achieve a given gas turbine reliability
requirement defined in the design stages, the reliability of
the end product is dependent on the final installation
process in the power plant site, including set up and testing
activities. The gas turbines are likely to be assembled under
field conditions that are more variable than those found in a
manufacturing plant and there is more cause for concern
that the reliability may be compromised. Very stringent
acceptance criteria on components (including reliable
packaging design to avoid both damage in shipment and
deterioration in storage), careful supervision and control of
the assembly and often an elaborate set of proofs or
acceptance tests are necessary in such situations.
The differences in reliability of turbines 1 and 2 can be
attributed to some slight differences in the assembly and set
up processes causing a great number of failures in the first
two years of operation, mainly those related to sensors
calibration.
Once the turbine has failed a corrective maintenance
procedure is performed to return the equipment to the
normal operation condition as soon as possible. The time to
repair is also a random variable since it is dependent on the
nature of failure, on the ability to diagnose the cause of
failure and on the availability of equipment and skilled
personnel to carry out the repair procedure.
The probability that an equipment will be repaired in a
given period of time is defined as maintainability and
described by a probability distribution. Typically the
lognormal distribution is used to model the time to repair
distribution of complex systems. The maintainability can be
expressed according to equation (3):
()
⎟
⎠
⎞
⎜
⎝
⎛−
Φ=
σ
μ
t
tM ln (3)
where:
M(t) maintainability at time t
µ lognormal distribution mean value
σ
lognormal distribution standard deviation
Φ(
•
) standard normal distribution cumulative function
Based on the time to repair database for both gas
turbines and using the software Weibull++ (Reliasoft,
2003), the lognormal distribution parameters for
maintainability modeling were calculated and are presented
in Table 3.
Table 3. Lognormal Distribution Parameters for Turbines'
Maintainability Calculation.
System Parameters
Gas Turbine 1
μ
= 1,52
σ
= 1,12
Gas Turbine 2
μ
=1,88
σ
= 1,95
The graphical representation of the maintainability
probability distribution for turbines 1 and 2 are presented in
Figure 6. As for turbine reliability, the points represent the
median rank plotting of each time to repair, arranged in
increasing order.
Figure 6. Turbines 1 and 2 Maintainability Distribution.
Turbine 1 has a mean time to repair smaller than turbine
2. This fact is explained through the analysis of the failure
database.
Turbine 1 has had simple failures, usually in association
with sensors or control system devices that require a short
time to repair, while turbine 2 presented a failure in the
lubrication oil system that required a time to repair greater
than 1000 hours. That value has a great influence on the
mean time to repair estimate.
For complex electrical-mechanical systems such as gas
turbines, the determining factors in estimating repair time
vary greatly.
In mechanical components, the causes of failure are
likely to be quite obvious. The primary time entailed in the
repair is then determined by how much time is required to
extract the damage parts and install the new components. In
contrast, if an electronic device (such as sensors or control
units) fails, maintenance personnel may spend most of the
repair procedure time in diagnosing the problem, for it may
take considerable effort to understand the nature of the
failure well enough to locate the part that is the cause.
Conversely, it may be a rather straightforward procedure to
replace the faulty component once it has been located.
Once the reliability and maintainability parameters are
calculated the system availability can be estimated.
Availability is a measure of the percentage of time that a
plant is capable of producing its end product at some

Int. J. of Thermodynamics (IJoT) Vol. 12 (No. 1) / 35
specified acceptable level. In the case of a gas turbine in a
power plant, availability is a measure of the fraction of time
that it is generating the nominal power output.
In a simple way, availability is controlled by two
parameters:
♦ Mean time to failure (MTTF) which is a measure of
how long, on average, the gas turbine will perform as
specified before an unplanned failure will occur, being
associated with equipment reliability;
♦ Mean time to repair (MTTR) which is a measure of
how long, on average, it will take to bring the
equipment back to normal serviceability when it does
fail.
Although reliability can be at least estimated during the
gas turbine design stages, its availability is strongly
influenced by the uncertainties in the repair time. Those
uncertainties are influenced by many factors such as the
ability to diagnose the cause of failure or the availability of
equipment and skilled personnel to carry out the repair
procedures. In the case of a gas turbine, the same
equipment model can present different availability in
different sites due to differences in the skill of personnel
responsible for maintenance.
Applying the Monte Carlo simulation method, the
availability can be estimated for an operation time.
That procedure uses a uniform distribution in the
interval (0,1) to generate a random time to failure or
random time to repair, drawn from the reliability and
maintainability distribution. The equipment first time to
failure (TF1) is a random number drawn from the reliability
distribution. Once failed, the time to repair the equipment is
drawn from its repair distribution (TR1). The equipment is
restored by time (TF1+TR1). The second time to failure,
randomly generated, is TF2 and the subsequent time to
repair is TR2. The time when the system is restored is
[(TF1+TR1) + (TF2+ TR2)]. This process is repeated over the
entire simulated operational time. The availability is the
ratio of the system uptime divided by the total simulated
time.
Considering the gas turbines operating over one year,
corresponding to 8760 hours, and using the reliability and
maintainability probability distribution presented in Tables
II and III, respectively, the availability for turbine 1 is
99.35% and for turbine 2 is 96%. Availability is an index
dependent on reliability and maintainability and
considering that turbine 1 has higher reliability and smaller
mean time to repair than turbine 2, clearly it must be more
available than turbine two. Using the method proposed by
Eti et al. (2007), the turbines availability are 99.46% and
91%, respectively for turbines 1 and 2, close to the values
defined by simulation.
For turbine 2 the simulated availability is higher than
the value estimated by the formula proposed by Eti et al.
(2007). The difference is caused by the use of mean time to
repair to define availability once that value is strongly
influenced by the 1000 hours time to repair associated with
one failure presented by turbine 2.
The North American Electric Reliability Corporation
(NERC, 2006) keeps available a reliability database based
on North America power plants’ performance that can be
used as a benchmark for power plants availability analysis.
According to that database the average availability of gas
turbines with nominal output higher than 50 MW, within
the period between 2002 and 2006, is 93.95%. The gas
turbines analyzed in the present study have higher
reliability than the values presented in the NERC database.
That comparison should be used only for initial evaluation
of the gas turbine performance since that database does not
clearly define the availability for heavy-duty gas turbines
and the average age of the turbines used in the database are
higher than the equipment evaluated in the presented paper.
Nevertheless, the performance of the gas turbines analyzed
in the present study can be considered satisfactory as far as
the availability index is concerned.
Considering that the failure rate of both gas turbines is
decreasing one can expect that the frequency of failures can
continue to decrease until the equipment reaches the
random failure stage. Based on the reduction of the failure
frequency, caused by maintenance policies or even
operational procedure improvement, an availability increase
can be expected.
The simulation results show that for complex electric-
mechanical systems, such as gas turbines, the availability
can be different for identical equipment located in the same
site. This difference can be associated with the operational
profile of the equipment, mainly the number of trips
presented during the operational life, and the nature of
failures – associated with mechanical or electrical devices.
3.5 Availability Improvement
The maintenance policy of gas turbines must follow
very stringent recommendations defined by the
manufacturer. Most of the maintenance procedure tasks,
involving periodical inspection and replacement of parts,
are related to parts submitted to very high temperature and
located in the hot gas path (combustion chamber plus
turbine). The parts that compose those subsystems can
present severe wear, affecting gas turbine performance. For
those parts the manufacturer did not allow any maintenance
procedure change once they are defined in maintenance
contracts involving equipment warranty. The periodic
inspection schedule is based on the number of equipment
start-ups and operational hours.
For auxiliary systems, such as the lubrication oil system,
the manufacturer recommends periodic inspections but does
not clearly define what kind of maintenance policies could
be applied to the components of those systems.
The results of the RCM analysis, listed as maintenance
tasks recommendations in Section 3.3, were presented to
the manufacturer who agreed to allow changes in the
lubrication oil system maintenance policy aiming to achieve
the power plant owner’s availability requirement.
That system is initially chosen once its failure has a
great impact on turbine operation, causing the equipment
trip to protect the sliding bearings.
In order to verify the feasibility of the changes in
lubrication oil maintenance procedures, the system installed
in turbine 2 (the one that presents the lowest availability)
had its design changed. Sensors were installed in the oil
pump to allow the use of a monitoring system to check oil
pump vibration and oil temperature and flow. Also a bi-
monthly oil analysis was implemented in order to check for
the presence of metallic particles in the fluid that could be
an indication of possible bearings parts wear. The trends in
those registers supported on condition maintenance tasks
planning to avoid wear-out failures in the pump and
unplanned outage of the gas turbine. Those monitoring
improvements were made during the five year overhaul
maintenance tasks.

36 / Vol. 12 (No. 1) Int. Centre for Applied Thermodynamics (ICAT)
After that change, gas turbine 2 operated for another
two years. The ‘time to failure’ and ‘time to repair’
databases of those two years are used to define the turbine
reliability, maintainability and availability, using the same
procedure presented in Section 3.2. The turbine was
considered ‘as good as new’ after the overhaul
maintenance, and the failures of the first five operational
years are not considered in the new reliability analysis.
In Table 4 the reliability and maintainability probability
distributions parameters are presented. The reliability
distribution is presented in Figure 7 and the maintainability
distribution is presented in Figure 8. The estimated mean
time to failure of turbine 2 increased to 2627 hours. The
shape parameter (
β
) of the Weibull distribution used to
represent turbine 2 reliability is close to 1 indicating that
the equipment is presenting random failure.
Table 4. Reliability And Maintainability Distribution
Parameters For Turbine 2 After Maintenance Procedure
Modification.
System Parameter of Interest Probability
Distribution Parameter
Turbine 2
Reliability (Weibull
distribution)
β
= 0 ,95
η
= 2562,05
Maintainability
(lognormal distribution)
μ
= 1,4
σ
= 0,86
Figure 7. Turbine 2 Reliability Distribution after
Maintenance Procedure Improvement.
Figure 8. Turbine 2 Maintainability Distribution after
Maintenance Procedure Improvement.
During those two years the oil pump had not presented
any unexpected failure proving the effectiveness of the use
of a condition based maintenance policy for critical gas
turbine components. All maintenance tasks could be
planned, reducing the number of unplanned or emergency
trips of the gas turbine.
The availability of gas turbine 2 was increased to
99.76% (estimate for one operational year), close to the
value calculated for turbine 1.
During those two years there were no more sensors
early failures, although there were failures regarding the air
inlet filtering system, blocking of inlet gas filters, and
overheating of some combustion chambers.
The results of the changes in the maintenance procedure
for turbine 2 lubrication oil system were approved by the
manufacturer and in the future will be extended to turbine
1.
4. Conclusions
In the design of few-of-a-kind systems, such as heavy
duty gas turbines, the designers and manufacturers’
experience and the use of standards and codes of good
practice are most beneficial in eliminating potential failure
mechanisms and modes from the system. Those facts help
to guarantee that the system may achieve a given reliability
performance.
For these kinds of systems only a few prototypes are
built (sometimes only one) and the preliminary reliability
tests are performed. Although the number of prototypes
available may not allow enough units of the final design to
be tested to failure to gain statistically meaningful
predictions of reliability, the failures that do occur are
valuable in that they add to the understanding of the failure
mechanisms and thus provide a basis for improving
reliability by modifying the design or through maintenance
procedures.
In the case of heavy duty gas turbines, the reliability
behavior can be quite different from the predicted values.
The turbine on-site installation process, operator’s skills,
maintenance crew training, environmental variables (air
temperature, humidity and solid particles concentration)
and gas quality, for example, can affect the reliability
behavior predicted theoretically and/or experimentally.
The data collected on field failures are particularly
valuable because they are likely to provide the only
estimates of the reliability and availability that incorporate
the loadings, environmental and maintenance procedure
effects found in practice. On both component and system
levels such a database is valuable for predicting on site
reliability and availability.
The proposed method for reliability and availability
analysis seems to be suitable for complex systems since it
allows not only the identification of critical components for
maintenance planning but also defines quantitatively the
system reliability and availability.
The development of the system functional tree is
important for the understanding of the functional relation
between system components. Based on the functional
hierarchy, the FMEA analysis is performed considering the
failure modes associated with the components listed in the
end of each branch of the functional tree, identifying the
effects of component failure on the system under analysis.
Once the critical components are identified, based on the
failure effects classification, a maintenance policy can be
formulated to reduce their occurrence probabilities.

Int. J. of Thermodynamics (IJoT) Vol. 12 (No. 1) / 37
The maintenance policy aims to reduce the system
unavailability through the use of predictive or preventive
maintenance tasks for critical components. This policy
allows the reduction of unexpected failure occurrences that
cause the system unavailability and are usually very
expensive to repair. For gas turbines the use of predictive or
preventive tasks seems feasible providing that a complex
monitoring system is applied.
The maintenance policy proposed by the turbine
manufacturer can be improved through the use of predictive
tasks in some auxiliary systems, such as the bearings
lubrication systems, since their failures can cause the gas
turbine trip. That improvement is feasible once most of the
auxiliary systems present some monitoring device.
Based on time to failure and time to repair data, the
method allows one to carry out system reliability,
maintainability and availability analyses.
For the case under analysis, which considered two
identical turbines installed in the same power plant, the
reliability was calculated considering a five-year
operational database. Both turbines have their reliability
represented by a Weibull probability function and are still
presenting a decreasing failure rate. Most of the failures
occurred in the first two operational years, associated with
sensors faults.
The reliability and availability are different for both
turbines. Turbine 1 presented a small number of failures
that were rapidly repaired having a small effect on system
availability. Turbine 2 presented almost twice the number
of failures of turbine 1 and had a high time to repair,
reducing the equipment availability. The availability of
turbine 2 was improved with the change of the maintenance
policy for the lubrication oil system, mainly through the use
of condition based maintenance.
The availability and reliability of the turbines presented
in the present study reflect on site behavior, including the
effects of changes in auxiliary systems maintenance policy.
Both gas turbines’ reliability and availability estimates can
be considered as preliminary. The equipment is designed
for a long operational life, more than 20 years, and the
analysis is based on the first five operational years. For that
period the operation personnel was still learning how to
deal with the equipment and the maintenance crew was still
getting used to the maintenance procedure. The
improvement of ‘time to failure’ and ‘time to repair’
databases during future operational years (with the addition
of more failure and repair data) will allow more reliable
estimates of the turbines reliability and availability.
Nevertheless those estimates can be used to check design
and maintenance procedures in order to adapt them to the
gas turbine local operational condition that may be different
from the average condition considered in the equipment
design. Those estimates can also be used for benchmarking
in order to compare the performance of the same gas
turbine model operating in different sites.
Acknowledgements
The authors thank the Brazilian Electrical Energy
Agency (ANEEL) and FUSP (USP Support Foundation) for
the financial support and Eng. Juliano Torres for the
technical advice.
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