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%

5%

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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 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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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.

REFERENCE

[1] Simmons, H.R. and Brun, K., Cheruvu, S., "Aerodynamic

Instability Effects on Compressor Blade Failure: A Root Cause

Failure Analysis," Proceedings of ASME Turbo Expo,

Barcelona, Spain, 2006.

[2] Latcovich, J.A., “Condition Monitoring and Its Effect on the

Insurance of New Advanced Gas Turbines,” Turbine Power

Systems Conference and Condition Monitoring Workshop,

Galveston, Texas, February 25-27, 2002.

[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

Journal, IEEE 9.11, p1527-1536, 2009.

[4] Momeni, S., Koduru, J. P., Gonzalez, M., and Godinez, V.,

“Online Acoustic Emission Monitoring of Combustion

Turbines for Compressor Stator Vane Crack Detection,” Proc.

SPIE 8690, Industrial and Commercial Applications of Smart

Structures Technologies 2013, March 29, 2013.

0%

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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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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