powergen gas turbine losses and condition monitoring: a
TRANSCRIPT
1 Copyright © 2014 by ASME
POWERGEN GAS TURBINE LOSSES AND CONDITION MONITORING -A LOSS DATA BASED STUDY
Bin Zhou FM Global Research Norwood, MA, USA
ABSTRACT In-situ condition monitoring (CM) is a crucial element in
protection and predictive maintenance of large rotating Power-
Gen equipment such as gas turbines or steam turbines. In this
work, selected gas turbine loss events occurring during a recent
ten-year period at FM Global clients’ power generation plants
were evaluated. For each loss event, a loss scenario or a chain
of failures was outlined after investigating the available loss
record. These loss events were then categorized based on the
nature of the associated loss scenario. The study subsequently
focused on the variables that could be monitored in real time to
detect the abnormal turbine operating conditions, such as
vibration characteristics, temperature, pressure, quality of
working fluids and material degradations. These groups of
condition monitoring variables were then matched with
detectable failures in each loss event and prioritized based on
their effectiveness for failure detection and prevention. The
detectable loss events and the associated loss value were used
in this evaluation process. The study finally concluded with a
summary of findings and path-forward actions.
Key word: PowerGen, gas turbine, loss investigation, condition
monitoring, risk mitigation.
1. INTRODUCTION Equipment losses contribute to a large portion of losses in
higher hazard occupancies such as PowerGen. While the fleet
of FM Global insured gas turbines has seen a steady growth
over the recent years, the reliability and availability of the gas
turbines have been continuously challenged by more taxing
loads, harsher environment and over-stretched operations to
meet rising power demand. Both operators and insurers of gas
turbines have become increasingly concerned with the risk of
turbine failures and associated losses. To help clients improve
loss prevention and reduce exposure to the risks, FM Global
continues its efforts to understand turbine loss scenarios and
failure mechanisms, and further, the risk mitigation methods
based on effective monitoring of turbine condition variables. Gas turbine loss events due to mechanical breakdown
during a recent ten-year period at FM Global clients’ power
generation plants were evaluated. While the true root causes of
individual loss events may have been ambiguous in a number
of cases, the study focused on the detection of the developing
failure at an early stage through condition monitoring, so as to
stop the failure progression or adjust the maintenance plan
accordingly to mitigate the risk of equipment loss. Each of the
reviewed loss events can be described as a loss scenario
comprised of a series of component failures. As a first step,
these loss events were categorized based on the nature of their
respective loss scenario. Such categorization improves the
understanding of major contributors of the turbine loss events.
Secondly, the failures in each typical loss scenario of the
categories led to the identification of appropriate condition
variables that can be monitored to detect these failures.
Relevant monitoring technologies were reviewed for each
group of condition monitoring variables. Finally, evaluation and
prioritization of the major types or groups of condition
monitoring variables were performed based on the effectiveness
of failure detection and prevention. The remainder of the paper
is structured following the steps of the study outlined above.
2. CATEGORIZED GAS TURBINE LOSSES Common gas turbines consist of stages of bladed rotors;
stationary components including vanes/nozzles, cases/frames,
combustors; and other accessory systems such as drive system,
control system, and piping systems for transporting lube oil,
fuel, air, water and steam. Mechanical failures can initiate at
system or component level and lead to the final turbine loss
event. System level failures include rotor-dynamics issues,
rubbing, compressor surge, contamination, control error and
off-design operations. At the component level, the various
Proceedings of the ASME 2014 International Mechanical Engineering Congress and Exposition IMECE2014
November 14-20, 2014, Montreal, Quebec, Canada
IMECE2014-38198
2 Copyright © 2014 by ASME
failure mechanisms include rupture, fatigue, creep,
corrosion/oxidation, erosion, fretting and impact.
Gas turbine mechanical failures occur rarely as isolated
events, but rather as a series of sequentially linked failure
incidents. The series of failures that contribute to the final loss
event is referred to as a loss scenario. A good example of a
typical gas turbine failure root cause analysis taken from the
literature is described below [1]. In this loss case, it was found
that the chain of failures started with a compressor surge caused
by an improper amount of inlet water injection. This resulted in
corrosion pitting and ultimately liberation of a third stage stator
vane due to high cycle fatigue (HCF). Impact damages as a
result of the liberated vane cascaded throughout the entire
turbine.
Various system and component level failures can occur in
gas turbines and result in catastrophic turbine losses. These
failure mechanisms are often linked and intertwined, and may
occur simultaneously or in sequence.
2.1 Turbine loss categories Useful categorization of the turbine loss events requires
recognition of not only the families of damaged components,
but also the loss scenario and the associated failure
mechanisms. In most cases, loss events can be categorized
based on first “known” family of failed components in a loss
scenario. In other cases, a group of loss events can be put into
one category because of their common distinctive failure mode
that would result in failures of multiple component families at
the same time. Each loss category may have one or multiple
typical loss scenarios that are similar in nature but different in
material failure mechanisms. Further explanation of the
categorization methods using examples of the loss categories is
included in this paper. While the true root cause of individual
loss events may have been ambiguous in a number of cases, the
categorization described above helps to identify condition
monitoring variables for failure detection at the early stages of
the turbine loss.
The present study was based on FM Global PowerGen gas
turbine losses during a recent ten-year period. The study
focused on the gas turbine losses due to mechanical breakdown,
which was a dominant 96% of the total PowerGen gas turbine
loss value. The loss events spanned over all major turbine
components. All loss events were grouped into thirteen
categories as shown in Table 2.1, each category representing a
typical loss scenario or a group of similar loss scenarios. Loss
category #1 for example, represents all loss events with
reported first known material failure of rotating blades and
subsequent domestic object damage (DOD) from released blade
mass. Typically, individual blade failure could be a result of
several potential material damage mechanisms, each of them
forming the basis of a unique failure scenario in this category.
These failure mechanisms may include blade vibration with
consequent HCF failure, blade thermo-mechanical damage,
creep rupture or hot corrosion, cracking of blade dovetail or
attachment portion. A given turbine loss event can be classified
into loss category #1, fitting either one of these loss scenarios.
Loss categories #2 and #4 were created with an emphasis on the
broad nature of loss scenario rather than a particular family of
failed components because these types of losses involve
damage of multiple families of components simultaneously;
and there was no clear indication of any incipient failure of a
single family of components. It should also be noted that
training and administrative control related issues would also
contribute to mechanical hardware failures; however, they were
not considered separately in the categorization process.
Table 2.1 Gas Turbine Loss Categories
2.2 Turbine loss data by loss categories
The reviewed gas turbine loss events were summarized in
terms of total loss value and numbers of loss events for each of
the thirteen loss categories outlined above. Figure 2.1 displays
the percentages of total loss values and loss counts for all loss
categories, ranked in descending order by total loss value. The
top four categories involve major blade damage, and the
combined loss value represents more than 80% of the total
value of the losses reviewed. Loss events in these categories
also involve casing damage due to rubbing, vane damage as a
result of DOD (Domestic Object Damage) or FOD (Foreign
Object Damage), and failures of attachment parts that are
normally considerably smaller in value compared to blade
damage. Loss dollar values due to blade damage further
increase because released material from damaged stator vanes
or combustors (loss categories#5 and #9) may result in
subsequent impact damage to the blades as well. The top four
categories in total loss value are consistently also the top four in
terms of numbers of losses. The blade failure initiated loss
events occur most frequently. The numbers of losses due to
attachments DODs and FODs or UODs (Unknown Object
Damage) ranked right after. This indicates that loss events as a
result of impact damages are very common in gas turbines.
The average loss value per event is compared in Figure 2.2
for all loss categories. These values represent the severity of
loss for each loss category. Rubbing is shown as the most costly
loss category, probably because it represents a type of system
level failure that involves multiple families of component types
including rotor, blades, bearings, vanes, seals and casing.
No. Loss Category Short Description
1 Blades crack and liberate causing domestic object damage (DOD)
2 Rubbing caused damage on blades and casing
3 Attachments break or detach causing DOD
4 Foreign or unknown objects damage (UOD/FOD)
5 Stator vanes crack and liberate causing DOD
6 Bearing damage (wiping etc.)
7 Seal leakage
8 Rotor (non-blade) damage (crack and imbalance etc.)
9 Combustor module crack
10 Piping leakage or blockage (including piping for air, fuel, oil, water/steam)
11 Control system error
12 Case/Frame crack
13 Gear teeth crack
3 Copyright © 2014 by ASME
Average total loss value due to initial blade failure is the next
highest on the list.
Figure 2.1: Percentages of Total Loss Value and Loss Count by Loss Categories
Figure 2.2: Normalized Average Loss Value by Loss Categories 2.3 Compressor losses vs. turbine losses
Compressor and turbine are the two main modules in the
gas turbine, each having a large number of rotating blades that
are susceptible to various kinds of damage. The nature of
mechanical failures in the two modules is however somewhat
different. Understanding such difference may help the
appropriate application of condition monitoring methods in
either module. Further investigation into the loss data was
conducted to understand the characteristics and the composition
of losses in compressor and turbine modules. In Table 2.2, loss
events due to failures in compressor and turbine are assembled
separately for relevant loss categories.* Red font color is used
to highlight loss categories where more loss events and loss
value are from the turbine module, whereas blue font color is
used to highlight loss categories where more loss events and
loss value are from the compressor module. Table 2.2: Gas Turbine Losses Compressor vs. Turbine
In the category of blade damage initiated events (Blade-
DOD), the number of loss events associated with the turbine
module is more than two times that associated with the
compressor; the percentage of total loss value is moderately
higher for the turbine. Turbine blades and wheels in general
experience more modes of failure mechanisms due to the high
temperature working environment, therefore would fail at a
higher rate. Rotor wheel cracking and following blade
liberation (a sub-group of the rotor loss category) also follows
the same trend for a similar reason. On the other hand, specific
features in the compressor such as the larger number of stages
and blade counts, unique failure mechanisms including surge
and flutter, largely increase the value per loss event due to
compressor blade failure.
Rubbing induced loss events occur dominantly in the
compressor module, due to the higher blade counts, larger blade
motion associated with aerodynamic design and instability, as
well as certain operational procedures such as water washing
and inlet fogging, which could trigger aerodynamic instability
in the compressor.
Impact damages also exhibit different characteristics in the
compressor and the turbine. The compressor is located
upstream and is therefore more susceptible to FOD coming
from the inlet, whereas the turbine is less susceptible to this
type of damage due to reduced debris momentum and reduced
chance of impact. Turbine modules on the other hand
experience a higher likelihood of DOD impact including debris
from blade rows, combustors, attachments and fasteners.
In summary, turbine module component failures result in a
larger number of loss events, whereas loss due to compressor
* In a few occasions, a single loss event was classified into two loss
categories. Some loss events incur both compressor and turbine failures.
0%
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45%
Loss Categories
% of Total Loss Value
% Loss Count
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Count 16 36
% Total Value 20% 24%
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% Total Value 21% 6%
Count 10 23
% Total Value 5% 8%
Count 14 11
% Total Value 9% 5%
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% Total Value 4% 2%
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Count 59 79
% Total Value 57% 43%
Blade-DOD
Rubbing
Other-DOD
UOD/FOD
Vane-DOD
Wheel-DOD
ALL
4 Copyright © 2014 by ASME
component failures is relatively more costly as shown in Figure
2.3. While the average turbine loss value was significant,
average compressor loss value was nearly twice of the amount.
The overall risk of compressor failure induced losses is higher.
Figure 2.3: Compressor vs. Turbine Losses
3. GAS TURBINE CONDITON MONITORING Turbine condition monitoring has been widely researched
and applied selectively by original equipment manufacturers
(OEMs) and turbine operators to address concerns of
equipment failures. Condition monitoring (CM), or sometime
also called health monitoring, is a process of continuously
monitoring parameters of operating conditions in turbines or
other machinery to identify significant changes as indications
of developing failures. As such, CM not only enables the
prompt activation of turbine protection mechanisms at
imminent failures to prevent losses, but also forms the core of a
condition based maintenance system, through which
maintenance schedules can be adjusted to effectively mitigate
the risk of turbine loss. Installation or upgrades of gas turbine
CM systems improves turbine reliability and availability in the
long run, and has great potential to be proven economically
viable and beneficial. Sophisticated CM systems are a necessity
for advanced gas turbine systems to prevent and mitigate
damage to high cost equipment, and the presence of risk-
reducing CM systems could allow insurers to take a more
positive view on insured hardware [2]. It should be clarified
that in-situ or online condition monitoring during turbine
operation does not eliminate the need for periodic inspections.
In this work, major types of condition monitoring variables
were categorized into six groups: i) blade vibration, ii)
rotor/case vibration and clearance, iii) temperature, iv)
pressure, v) quality of fluids and vi) cracking or degradation of
material. Although the direct monitoring of material cracking or
degradation can potentially be applied to any components, such
monitoring is most practical for stationary components such as
stator vanes and cases.
3.1 Blade vibration monitoring Blade vibration characteristics including natural
frequencies, mode shapes, vibration amplitude and phase, are
all functions of several key parameters, i.e. mass, stiffness,
damping and excitation. These parameters in turn depend on the
airfoil external and internal geometry, material, attachment or
support methods, and various excitation sources. Any changes
of these factors will alter the blade vibration characteristics and
therefore potentially be detected. Figure 3.1 depicts the cause
and effect relations among factors at different levels leading to
blade vibration characteristics. It is apparent that blade
vibration represents a critical variable for condition monitoring
because: i) over 80% of the gas turbine loss value involves
some kind of blade damage that changes the blade geometry or
stiffness and hence the vibration signature, in multiple loss
categories, ii) off-design or abnormal operating conditions often
manifest themselves in blade vibration characteristics, and iii)
status change of blade attachments affects the stiffness and
damping of the bladed rotor system and thus also changes the
blade vibration signature.
Figure 3.1: Blade Vibrations: Cause and Effect
During turbine operation, practical factors such as hostile
environment, FOD/DOD, loss of attachment and off-design
operating practices preclude the use of surface mounted strain
gages on rotating blades due to their poor durability and
associated high cost of instrumentation. With these limitations,
turbine operators are only left with non-contact vibration
monitoring systems for monitoring of the rotating blades. Case
mounted NBVM (non-contact blade vibration measurement)
sensors pick up a signal as each blade passes by and the blade
tip time of arrival (TOA) is measured to infer blade vibration
amplitude and frequency. Compared to strain gages, non-
contact blade vibration measurement techniques have higher
durability and also cover all blades in a given row.
Resonance frequencies as well as vibration amplitude and
phase of concerned blade vibratory modes are captured by the
NBVM system. Significant % shift of resonant frequency of an
individual blade may be an indicator of blade damage and/or
loose attachment. The high vibration response amplitude of all
blades due to any type of abnormal vibration can be monitored
in real time for violations against alarm and trip limits to
protect the turbine from hardware damages automatically. The
blade static positions are observable to NBVM systems in
addition to the vibration signatures. While the original blade
static positions at a given speed and power setting are dictated
by as-manufactured geometry and assembly, the unique
positional signature can be altered at the same running
condition by rubbing, surge, cracking and impact damage. The
blade row static position can be continuously monitored with
imposed protection limits on deviation from the original profile.
0
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Loss
Co
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f To
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Val
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Fatigue
ErosionImpact
Corrosion
Creep
Deforma
-tion
Wear
Stall
Frequency
Mode Shape
Response
(Amp & Phase)
Mass Stiffness Damping Excitation
Geometry Material Attachment
Surge
Sync
LoadFlutter
Loose
Joint
Low
Flow
5 Copyright © 2014 by ASME
3.2 Rotor/Case vibration and clearance monitoring These condition variables are grouped together not only for
their intrinsic cross relations but also because the monitoring
technologies involved are the same or similar.
Vibration of the rotor in a gas turbine is a system level
issue and can lead to damage of virtually any turbine
component. Root causes of rotor vibration include, but are not
limited to, damaged/worn bearings, imbalance, rotor bow,
misalignment, improper mounting, rubbing, undesired bearing
and seal fluid conditions, and aerodynamic instabilities. These
factors are interconnected and contribute to the change of the
rotor’s natural vibratory modes or critical speeds, the
mechanical, fluid-dynamic and aerodynamic loading, as well as
the system damping. Vibration of the casing and frames is
normally linked to rotor vibration through bearings, although it
may also come from other sources such as foundations. Rotor
vibration and blade vibration can lead to each other. While
blade damage and material loss will cause rotor imbalance and
vibration, rotor vibration will result in blade tip rubbing,
cracking and further DOD damage downstream.
Blade row tip clearance is an important parameter for
turbine operability and performance. Tight tip clearance helps
to maintain turbine performance but at the same time makes the
tip prone to rubbing. The main source of rubbing is rotor
eccentricity or run-out, which often times is associated with
rotor vibrations. Other sources of clearance change may include
casing contraction and blade growth.
Rotor lateral vibrations can be detected by measurement on
displacement, velocity and acceleration. This leads to common
sensing technologies including proximity probes,
accelerometers, and velocity transducers. Rotor torsional
vibration on the other hand, is normally measured by torque
sensors. Proximity probes are often mounted on plain/fluid
bearings to directly monitor the vibration of a rotating shaft,
through the measurement of relative motion between the rotor
and the bearing without contact. When dually used as blade row
tip clearance or blade vibration monitors, these probes are
mounted on the casing directly above the blade rows, with
connected data acquisition and analysis systems adjusted
accordingly. Different from heavy industrial turbines,
aeroderivative turbines or gas generators use rolling element
bearings, where case mounted accelerometers or velocity
sensors for vibration measurements are required, as no relative
movement between the shaft and the rigid bearing would occur.
Case and frame mounted sensors can also be used to detect high
vibration leading to high cycle fatigue of these structures.
3.3 Temperature, pressure and performance monitoring Temperature measurement are widely applied to monitor
conditions for lube oil, fuel, air (exhaust spread, inlet,
extraction and injection, rotor stages, etc.), water or steam
injection, and metal (usually bearing). Typically, excessive
temperature represents an early sign of malfunction or failure.
Temperature monitoring is normally done with temperature
sensors including thermocouples (TCs) and resistance
temperature detectors (RTDs), as well as non-contact infrared
thermometers when applicable.
Static or steady-state pressure sensors measure pressure at
a steady state for a give operating condition at a given location.
Measurements include stage pressures, fuel pressure, lube oil
pressure, inlet filter delta pressure, and exhaust backpressure.
Abnormal measured pressure may be an indicator of leakage,
blockage, or aerodynamic instability. The so called “dynamic”
pressure sensors or unsteady pressure sensors measure
pressures that change rapidly. For gas turbines with lean or dry
low NOx combustion, the pressure pulsations in the combustion
chamber are commonly monitored to avoid the onset of
combustion instability that would cause failure by HCF of
combustor components.
Performance monitoring uses measured temperature,
pressure and/or flow measurement to calculate heat balance or
turbine efficiencies, and power output online during turbine
operation. Performance monitoring allows proactive responses
to incoming failures, understanding of long-term trends of
deterioration and performance degradation so as to improve the
scheduling of maintenance. Measurement of mass flow is
typically inferred from measurements of pressure and
temperature.
3.4 Fluid quality monitoring The broad reference to “fluid” includes lube oil, water, as
well as gas fuel, air and steam. Condition monitoring in this
category mainly refers to the monitoring of contamination and
degradation of these fluids of either a chemical or physical
nature, in addition to performance parameters such as
temperature, pressure and flow. While such monitoring is often
based on periodic fluid sampling and testing, there are existing
systems for real-time monitoring of lube oil particles/debris,
water content, steam purity, and other properties.
Condition monitoring of lube oil should focus on
cleanliness, particle counts, water content, total acid number,
viscosity and corrosive elements, to identify contamination and
degradation as well as the signs of deterioration of bearings and
gears.
Besides maintaining a functioning inlet filtering system,
quality of inlet air needs to be monitored or periodically
checked for solid objects and liquid contaminants including
water or oil droplets and water-dissolved corrosive chemicals
that can cause FOD, erosion, fouling and corrosion. Monitoring
of contaminants in exhaust emission should also be applied to
comply with environmental regulations and to assist inlet air
quality analysis.
A number of contaminants including solids, water, other
combustibles, and chemicals may potentially exist in the fuel.
These contaminants should be monitored to prevent hot
corrosion, fouling, combustion issues, injectors coking, and
excessive emissions. For liquid fuels, monitoring of viscosity is
also a key for smooth combustion.
Water or steam enters into a gas turbine from inlet fogging
and inter-stage cooling, combustor steam injection, water
washing, and turbine cooling injection. Purity of the water or
6 Copyright © 2014 by ASME
steam used in the turbine operation should be monitored and
contaminants should be removed through a demineralization
process to reach the required quality. These contaminants can
cause airfoil deposit buildup which result in aerodynamic
instability, performance reduction, rotor-dynamics issues,
erosion, and corrosion.
3.5 Monitoring material cracking or degradation
Turbine losses due to failure of stationary components,
including stator vanes, cases and frames, can be substantial.
Stator vane failure in particular is significant and ranked #5
both in terms of total loss value and event counts. A large
number of stator vanes are placed between the blade rows in the
turbine flow path. These vanes are exposed to aerodynamic
loads, thermal attack, corrosion, erosion and impact by flying
objects. Broken pieces of vanes may further cause impact
damage to blades and other components. Most methods of
vibration monitoring may not be practical or even feasible for
vanes. Vibration transducers for example can be mounted only
on the casing and frame in a minimum amount, but cannot be
applied on stator vanes due to blockage of flows that would
reduce the turbine performance. Strain gages on the other hand
are limited by counts and durability concerns.
While direct material failure detection methods are
normally used for field non-destructive evaluation (NDE), a
couple of these such as Acoustic Emission (AE) and
Meandering Winding Magnetometer (MWM) [3] have shown
potential for continuous condition monitoring. Acoustic
emission technology for example can measure transient elastic
waves propagating within a material due to the release of
localized stress/strain energy in terms of material failure,
friction, impact and fluid blockage/leakage. A group of AE
transducers can also be used to locate the sources of events by
measuring the travel time of the wave. A very recent study has
shown that AE sensors were able to detect the location of an
event of stator vane cracking during turbine operation [4].
However, these advanced methods for failure detection have
seen very limited successful industrial applications, especially
in gas turbines
4. PRIOTIZATION OF CM FOR TURBINE LOSS PREVENTION To evaluate the effectiveness of condition monitoring for
failure detection, the identified groups of condition variables
were linked with the detectable failures of a given loss event.
Figure 4.1 illustrates a typical blade HCF induced loss event
along with applicable condition monitoring at various stages of
the event. Such a loss event may be generally described by a
category #1 (see Table 2.1) loss scenario. As shown in the
figure, blade vibration monitoring can be effective during
various stages of the loss scenario. Fluid quality monitoring
may help to reduce corrosion, therefore preventing or delaying
the blade fatigue failure initiated at corrosion pits. Monitoring
of rotor vibration and/or blade tip clearance would also be
effective, but anomalies would be detected only after blade
mass loss occurs. Temperature, pressure and performance
monitoring may also indicate anomalies subsequent to blade
mass loss. It can be noted that a loss event could be considered
detectable by multiple condition variable groups.
Figure 4.1: A Blade Failure Scenario and Monitoring Variables
As the main groups of condition monitoring variables were
linked to detectable failures in a given loss event, the event
itself was deemed detectable by the corresponding condition
variable(s). The groups of condition variables were
subsequently evaluated for the effectiveness of failure detection
based on their detectable loss events and the associated loss
value. Figure 4.2 shows a comparison of the percentages of
total loss value, which can be detected by each group of
condition monitoring variables. Both blade vibration and rotor
vibration monitoring can effectively detect a dominant portion,
i.e. 94% and 97% respectively, of the total loss value.
Detections of abnormal temperature, pressure, quality of fluids,
and material cracking or degradation on stator vanes and cases
rank lower with 45%, 16%, 10% and 6% of the total loss value
of all the loss events. A consistent trend or ranking can be
observed on the percentage of loss counts detectable by
different monitoring variable groups. The loss events attributed
to the top two groups were further investigated in order to
differentiate the sequence and promptness of the failure
detection. As shown in Figure 4.3, 50% of the total loss value
detectable by blade monitoring is actually initiated by the blade
itself or FOD, with the other 44% attributed to secondary blade
damages following other component failure induced DOD,
rubbing and other failures. On the other hand, only 29% of the
total loss value detectable by rotor vibration monitoring is
actually initiated by the rotor system itself, including typical
rubbing, bearing vibration and damage; the other 68% is due to
rotor vibration following blade failure and loss of mass induced
imbalance. This observation implies that blade vibration
monitoring is a better option to detect the majority of vibration
related failures at earlier stages of the loss.
Blade
Vibration
Pitting
& Notch
Blade HCF
Crack
Blade Mat.
Loss
Rotor
ImbalanceRubbing
Impact/DOD
Fluid Quality
Blade Vibration (NSMS)
Rotor Vibration and Clearance
Performance
Reduction
Abnormal
Temperature
Abnormal
Pressure
Temperature
Pressure
Performance
System Failure
7 Copyright © 2014 by ASME
Figure 4.2: Percentages of Total Loss Value and Loss Counts
Detectable by Groups of CM Variables
Figure 4.3: Blade and Rotor Vibration Monitoring
5. CONCLUSIONS Loss events reported as PowerGen gas turbine mechanical
breakdown in a recent ten-year period were reviewed. Loss
events were grouped into thirteen loss categories that supported
the understanding of typical gas turbine loss scenarios and
associated failure mechanisms. Major groups of condition
variables were subsequently identified so that continuous real-
time monitoring of these variables would lead to detection of
failures and improve loss prevention. Failure detection and
monitoring technologies associated with these variables were
also reviewed. Lastly, identified groups of condition monitoring
variables were evaluated and prioritized based on the
effectiveness of failure detection for these known losses. The
summary of findings is presented below.
Blade damage related turbine loss represents a dominant
fraction of the total loss value and number of loss events
reviewed in the study.
Vibration monitoring on blades and rotors was found most
effective in the detection of developing mechanical failures
in gas turbines, and should therefore be given higher
priority. Blade vibration monitoring also has the advantage
of detecting vibration related failures earlier.
The present study only provides a prioritization of the
condition monitoring variables based on their effectiveness
of failure detection. Further reviews including cost–benefit
analysis would help to understand the practicality and
feasibility of advanced monitoring technologies.
ACKNOWLEDGMENTS Support from Kumar Bhimavarapu, William Doerr, Erik
Verloop and Franco Tamanini in reviewing and discussing the
contents of the manuscript is greatly appreciated.
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[3] Sheiretov, Y., Grundy, D., Zilberstein, V., Goldfine, N., and
Maley, S., “MWM-Array Sensors for In Situ Monitoring of
High-Temperature Components in Power Plants.” Sensors
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[4] Momeni, S., Koduru, J. P., Gonzalez, M., and Godinez, V.,
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0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Blade Vibration
Rotor/Case Vib &
Clearance
Temperature Pressure Fluid Analysis Stator/Case Crack
CM Variable Groups
% of Total Loss Value
% Loss Count
50%
29%
44%68%
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40%
50%
60%
70%
80%
90%
100%
Blade Vibration Monitoring
Rotor Vibration Monitoring
% o
f To
tal L
oss
Val
ue
Vibration Detection Techniques
Ensuing Damage
Initial Damage
Blade InitialDamage
Post Rub & DOD
Rotor Initial Vibration,Rub
Post BladeDamage