Risk Based Inspection and Maintenance Systems for Steam Turbines

Risk-based inspection and maintenance systems for steam turbines

Kazunari Fujiyamaa,*, Satoshi Nagaia, Yasunari Akikunib, Toshihiro Fujiwarab,Kenichiro Furuyab, Shigeru Matsumotob, Kentaro Takagib, Taro Kawabatac

aPower and Industrial Systems R&D Center, Industrial and Power Systems and Services Company, Toshiba Corporation, Yokohama, JapanbThermal and Hydro Power Division, Industrial and Power Systems and Services Company, Toshiba Corporation, Tokyo/Yokohama, Japan

cKeihin Product Operations, Industrial and Power Systems and Services Company, Toshiba Corporation, Yokohama, Japan

Abstract

The risk-based maintenance (RBM) system has been developed for steam turbine plants coupled with the quick inspection systems.

The RBM system utilizes the field failure and inspection database accumulated over 30 years. The failure modes are determined for each

component of steam turbines and the failure scenarios are described as event trees. The probability of failure is expressed in the form of

unreliability functions of operation hours or start-up cycles through the cumulative hazard function method. The posterior unreliability is

derived from the field data analysis according to the inspection information. Quick inspection can be conducted using air-cooled borescope

and heat resistant ultrasonic sensors even if the turbine is not cooled down sufficiently. Another inspection information comes from

degradation and damage measurement. The probabilistic life assessment using structural analysis and statistical material properties, the latter

is estimated from hardness measurement, replica observation and embrittlement measurement. The risk function is calculated as the sum

product of unreliability functions and expected monetary loss as the consequence of failure along event trees. The optimum maintenance plan

is determined among simulated scenarios described through component breakdown trees, life cycle event trees and risk functions.

Those methods are effective for total condition assessment and economical maintenance for operating plants.

q 2004 Elsevier Ltd. All rights reserved.

Keywords: Risk-based maintenance; Steam turbine; Database; Inspection; Event tree; Unreliability; Life cycle; Failure; Damage

1. Introduction

In Japan, the cost-effective maintenance of power plants

is required under the trend of more competitive power

generation market. Recently, risk-based maintenance

(RBM) has been introduced to fossil power plants for the

solution of utility’s requirements [1]. The object of

introducing RBM is to provide the rational basis of decision

making for life cycle maintenance planning. There are three

categories in the risk assessment, that is, qualitative,

semi-quantitative and quantitative approach. The semi-

quantitative approach is widely used for various plants,

being well known as the risk ranking matrix approach.

The quantitative approach has the advantage to solve the

optimization problem numerically, which enables to apply

various mathematical tools.

0308-0161/$ - see front matter q 2004 Elsevier Ltd. All rights reserved.

doi:10.1016/j.ijpvp.2004.07.005

* Corresponding author.

E-mail address: [email protected] (K. Fujiyama).

The RBM system has been developed to perform the

probabilistic risk analysis coupled with inspection systems.

The RBM system comprises life cycle event trees,

unreliability function analysis for field failure database

and risk-cost analysis for various maintenance scenarios.

Unreliability represents the failure probability here as the

function of operation hours and number of starts. The basis

of unreliability analysis is the statistical database of field

failure and damage related to the operation history.

For global application of the quantitative RBM method,

various ways are considered to compensate for the lack of

statistically meaningful number of data. The RBM system

can be customized to specific users by modifying the unified

master curve for unreliability analysis.

For customizing the system, the inspection information

of specific unit is useful for obtaining posterior unreliability

functions by modification of prior unreliability functions.

To reduce outage time, the air-cooled borescope and the

heat resistant ultrasonic sensors are provided for turbine

inspection before the turbine is not cooled down sufficiently.

International Journal of Pressure Vessels and Piping 81 (2004) 825–835

www.elsevier.com/locate/ijpvp

http://www.elsevier.com/locate/ijpvp

K. Fujiyama et al. / International Journal of Pressure Vessels and Piping 81 (2004) 825–835826

Life assessment information is also useful for obtaining

probability of creep and fatigue cracking life by stochastic

simulation analysis. With several examples, it is

demonstrated here how the quantitative RBM system

helps the decision making on life cycle plant maintenance

planning and economical management.

2. Basic flow of the risk-based inspection

and maintenance procedure

Fig. 1 shows the basic flow of the risk-based inspection

and maintenance procedure. Each step has the role as

follows:

(1) Component breakdown trees. A steam turbine unit

can be divided into many components. The level of

component breakdown might be decided according to the

level of maintenance action.

(2) Life cycle event trees. Though the event tree is usually

expressed as the sequence of success/fault nodes, it is used

here for describing the chain action of one component

failure leading to another component failure because steam

turbine components are closely assembled each other and

rotating in high speed.

(3) Master field database. The failure, inspection and

repair history database is established for various types units

over 30 years. The database is formed as a relational

database of unit, components, location, event and operation

history.

(4) Unreliability analysis. The failure probability is

defined here as the unreliability. The cumulative hazard

function method is used for deriving the unreliability

functions of operation hours or start-up cycles.

(5) Inspection and life assessment. The unit specific

unreliability functions are obtained as the posterior

unreliability functions after detected event from inspection.

For degradation and damage accumulation phenomena,

probabilistic life assessment is used for simulating future

unreliability.

(6) Risk assessment. The risk is defined here as the sum

product of the unreliability functions and the expected

Fig. 1. Basic flow of risk-based inspection and maintenance procedure.

monetary loss due to the accidents along the scenario of life

cycle event trees.

(7) Maintenance planning. Maintenance scenarios are

planned as the life cycle sequence of failure events and

related preventive actions. The risks and preventive costs

are calculated over the total life cycle for selecting the

optimum maintenance scenario.

3. Damage and failure modes of steam turbines

Fig. 2 shows a component breakdown tree of a steam

turbine unit. Those components show various types of

degradation, damage and failure phenomena according to

temperature, stress, environment and materials.

Fig. 3 shows degradation, damage and failure modes of

steam turbine major components [2,6]. For high- and

intermediate-pressure (HIP) portions, the typical events

are creep induced deformation, thermomechanical fatigue

cracking and steam flow induced erosion. For low pressure

(LP) portion, the typical events are environmental

assisted fatigue cracking and steam flow induced

erosion. The features of events are described as follows

for major components.

(1)
HIP rotor. High centrifugal stress and high temperature
cause creep deformation such as rotor bowing. The rotor

bowing causes vibration and rubbing with bearings and

casings. Creep damage accumulation causes creep void

Fig. 2. Component breakdown tree.

Fig. 3. Degradation, damage and failure modes of steam turbine major

components.

K. Fujiyama et al. / International Journal of Pressure Vessels and Piping 81 (2004) 825–835 827

formation and cracking at highly stressed portions such

as bore and wheel hooks. Thermomechanical fatigue

damage accumulation causes cracking at the wheel

corner portion.

(2)
LP rotor. High centrifugal stress, high vibratory stress
and corrosion environment causes corrosion fatigue at

the wheel section.

(3)
HIP moving blade. High centrifugal stress and high
temperature causes creep deformation such as lifting.

Fig. 4. An example of event trees fo

The lifting causes rubbing with casings or nozzles and

finally cracking. Creep damage accumulation causes

creep void formation and cracking at the highly stressed

portion such as dovetail hooks. Oxide scale brought by

steam flow causes erosion. Vibratory stress causes high

cycle fatigue cracking and fretting fatigue at the contact

portion.

(4)
LP moving blade. High centrifugal stress, high vibratory
stress and corrosion environment causes corrosion

fatigue cracking. Droplet brought by steam flow causes

erosion.

(5)
HIP nozzle. Oxide scale brought by steam flow cause
erosion. Pressure difference at each stage and high

temperature cause downstream deflection of nozzle

diaphragm.

(6)
HIP casing. High pressure stress and high temperature
cause creep deformation. The creep deformation causes

assembling mismatch and steam leak due to stress

relaxation at the flange and the tightening bolt. Creep

and thermomechanical fatigue damage accumulation

causes cracking at the nozzle fit radius and other stress/

strain concentration portions.

(7)
Valve. Creep and thermomechanical damage is the same
as casings. Oxide scale brought by steam flow causes

erosion at the shield plates. Oxidation at the shaft and

valve body contact portion causes valve shaft sticking.

(8)
Pipe. Creep damage accumulation causes creep void
formation and cracking preferably at the weld portion.

Water induction causes thermomechanical or thermal

shock cracking.

Fig. 4 shows the event trees coupled with component

breakdown trees based on the above information. This is the

basis of the following analysis.

r steam turbine unit.

Fig. 5. Cumulative hazard functions for turbine rotor bowing fitted

individually.

Fig. 7. Unified cumulative function for rotor bowing.


4. Unreliability analysis

4.1. Master field database

The master field failure database has the data rows of

plant name, component name, occurrence date, operation

hours, start-up cycles, event contents, event cause and repair

actions. Every event is related to operation hours and start-

up cycles until the events occur. For the events detected at

the scheduled inspection, the estimated hours and cycles are

adopted using reference operation history tables.

4.2. Field data analysis method: Cumulative hazard

function method [3]

The unreliability function is derived through cumulative

hazard function method for the field failure database. The

event data are stacked in the order of time or cycles for the

same mode of failure and the same type of turbines.

Estimated cumulative hazard function is expressed as

follows

HðtkÞ ¼Xk

i¼1

1

n þ 1 K i(1)

Fig. 6. Unreliability functions for turbine rotor bowing fitted individually.

where, tk is event occurrence time at the k-th event, n is total

number of samples including non-failure data.

Regression of cumulative hazard function H(t) is

conducted using the two-parameter Weibull plot expressed

in the following equations.

HðtÞ Z ðt=hÞm (2)

ln HðtÞ Z m ln t Km ln h (2 0 )

where h, m are regression constants.

Unreliability function F(t) is calculated as follows

FðtÞ Z 1 KRðtÞ Z 1 KexpfKHðtÞg (3)

where R(t) is reliability function.

Eqs. (1)–(3) are also applied for cycle N dependent

events using N instead of t. To overcome the lack of

sufficient numbers of data, two approaches are adopted.

One is the unified unreliability function approach and the

other is the empirical unreliability function approach. Those

two approaches are described below.

4.3. Unified unreliability function approach:

example of rotor bowing

If the dominant parameter of an event is known, unified

master curve can be obtained by normalization. Here, rotor

Fig. 8. Unified unreliability function for rotor bowing.

Fig. 11. Machine output class dependence for cycles at 50% unreliability

for nozzle erosion events.Fig. 9. Hours based cumulative hazard functions.


bowing phenomena is taken as an example though this event

is prevented now due to the improvement of manufacturing

and design.

Figs. 5 and 6 show the cumulative hazard functions and

unreliability functions against operation hours for two types

(A-type and B-type) rotors. The type-B rotor regression is

conducted with only two events. Here, the hazard function

fitting against time (operation hours) is better than that

against number of starts as reported elsewhere [5]. The time

dependence comes from that the rotor bowing is one of the

creep deformation phenomena. It depends on stress,

temperature and material conditions. As the rotor bowing is

one of the creep phenomena, the time is normalized by

creep rupture time, that is, t/tr. Figs. 7 and 8 show the good

unique correlation of cumulative hazard functions and t/tr for

the two types of turbines, expressed by the following

equation.

HðtÞ Zt

trðs; T ; lÞ

� �=hc0

� �mc0

(4)

where hc0, and mc0 are regression constants, s is stress, T is

temperature and l is material strength parameter such as

hardness or tensile strength, etc.

Fig. 10. Cycle based cumulative hazard functions.

Eq. (4) indicates that the unreliability function of the

specific unit can be estimated only by knowing design

conditions or service conditions and material properties.

4.4. Empirical approach: example of nozzle erosion

In the case of nozzle erosion, It is difficult to find the

explicit parameters dominating erosion process. Figs. 9

and 10 show total regression results of cumulative hazard

functions against operation hours and number of starts,

respectively, expressed by the following equations.

HðtÞ Z ðt=ht0Þmt0 (5)

HðNÞ Z ðN=hN0ÞmN0 (6)

where ht0, hN0 and mt0, mN0 are regression constants

Eqs. (5) and (6) fit the whole data well enough but still

indicate discrepancy between the turbine output types at

some extent. Here, we introduce a modifying approach

using hours or cycles at 50% unreliability based on the

unified unreliability function. We show the modification

results for cycle dependent unreliability functions or

cumulative hazard functions.

Fig. 12. Empirical cumulative hazard functions of erosion for various

nozzles.

Fig. 13. Empirical unreliability functions of erosion for various nozzles.


Fig. 11 shows the relationship between number of starts

at 50% unreliability N50 and output class index I that is

proportional to output capacity. N50 shows almost

monotonic decreasing relationship with I, expressed as

follows

N50 Z aIb (7)

where a and b are regression constants.

Fig. 15. Heat resistant UT system (left: application

Fig. 14. Air-cooled borescope v

Modified cumulative function is obtained by using the

modifying coefficient N50/N50,0, where N50,0 is number of

starts at 50% unreliability for the unified curve of Eq. (6).

HðNÞ Z NN50

N50;0

� �=hN0

� �mN0

(8)

Figs. 12 and 13 show the estimation results if the

modifying coefficient approach. The estimation curves fits

actual data reasonably even for the insufficient data.

5. Inspection system and unreliability function

5.1. Quick visual and ultrasonic inspection system

The visual and ultrasonic inspection gives useful

information for adjusting the prior unreliability functions.

To reduce the outage time for inspection, quick inspection

systems have been developed.

Fig. 14 shows an air-cooled borescope inspection system

for nozzle erosion/failure detection. Magnet wheels

attached to the inspection head move along the nozzle

front, and remote observation by CCD camera is easily done

to components; right: the detail of carriage).

isual inspection system.

Fig. 18. Life assessment procedures based on an

Fig. 16. Prior and posterior unreliability functions for moving blade erosion.

Fig. 17. Life assessment system coupled with


at temperature below 300 8C after machine shutdown.

This may require about a couple of days.

Fig. 15 shows a heat-resistant UT system for casing or

valve defect detection. The moving head contains a

couple of heat resistant UT sensors with the supply

system of high temperature coupling medium to attach

the system to a hot wall of about 300 8C. This system is

used for detecting casing or valve inner defect and bolt

cracking.

Fig. 16 shows the posterior unreliability functions of

moving blade erosion calculated by the prior unreliability

function of nozzle erosion and the prior unreliability of

moving blade erosion. Cycles to erosion event of nozzle is

subtracted from cycles to erosion event of moving blade.

alysis and non-destructive measurement.

non-destructive measurement system.


The cycle difference values and cumulative hazard

function values are coupled and fitted again by Eq. (2).

The obtained posterior unreliability function shows

higher unreliability for small cycles indicating

immediate occurrence of moving blade erosion after

nozzle erosion.

Fig. 20. Cumulative probability of creep rupture life ratio derived from the

unified master curve.

5.2. Degradation/damage measurement and life

assessment system [4,5]

Fig. 17 shows a degradation/damage measurement

and life diagnosis system schematically. Degradation

and damage are measured by the hardness

measurement system, replica observation technique and

embrittlement measurement system. Life assessment

system is programmed to calculate creep and fatigue life

calculation using evaluation master curves and machine

information.

Fig. 18 shows the deterministic life assessment pro-

cedures [7]. Creep and fatigue damage is calculated by

cumulative damage rule using the life assessment master

curves. The feature of the procedure is that life assessment

master curves are derived from material condition data

measured by the hardness measurement system and the

embrittlement measurement system. Creep and fatigue life

evaluation curves are derived from hardness values

measured for post-serviced components. Crack growth

rate and fracture toughness are derived from FATT value

converted from electrochemical polarization parameters

using experimental master curves.

Probabilistic life assessment requires statistical material

properties [5,8]. Fig. 19 shows material creep rupture data

including unused, laboratory aged and service used plotted

Fig. 19. Unified plot and regression of creep rupture for unused, aged and

serviced materials.

using stress/hardness ratio and Larson–Miller parameter.

Fig. 20 shows the unified statistical distribution of

experimental/estimated creep life ratio based on the

whole creep rupture data and the master regression

curve. It can be used as the simulated unreliability

function of creep life of actual component. When

narrower distribution is required, the data for statistical

analysis should be selected carefully according to the

specification of manufacturer.

Fig. 21. An example for the optimization of maintenance interval for rotor

bowing.

Fig. 22. PC based RBM system window view of risk assessment of rotor bowing.


6. Risk assessment and maintenance planning [5,6]

Risk is defined here as the sum product of unreliability

functions and expected monetary loss for every event in

the event trees. The risk functions are specified by plant

information and inspection information. Monetary loss is

calculated for all expected items related to unscheduled

outage and recovery action. Two ways of optimizing

maintenance planning are presented below, that is, the

optimization of maintenance intervals and the optimization

of life cycle maintenance scenarios.

Fig. 23. Event trees, unreliability functions and risk functions for nozzle

erosion.

6.1. Maintenance interval optimization: example

of rotor bowing

Fig. 21 shows an example of the optimization of

maintenance intervals for rotor bowing. The event tree is

restricted to include typical three events for simplification.

Those three events: (a) rotor bowing; (b) narrow axial

clearance; (c) vibration, have different risk functions. The

total risk function is determined by the sum of the three risk

functions. The maintenance cost index is defined as the total

cost of preventive maintenance action averaged per year for

the subscribed events. The total risk is increasing function of

operation hours and the cost index is proportional to

Fig. 24. The optimization of life cycle maintenance scenario for nozzle erosion.

Fig. 25. Application concept for risk-based engineering.


the reciprocal of maintenance hours-based interval.

The total cost curve is obtained by the sum of risk and

maintenance cost showing a concave curve. If the income by

operation is proportional to operation hours, the shaded area

is recommended to decide the optimum maintenance

intervals. The rotor bowing events are decreasing currently

due to the improvement of manufacturing process, design

and operation.

Fig. 22 shows an example of the personal computer

based RBM system window view of risk assessment of

rotor bowing.

6.2. Maintenance scenario optimization:

example of nozzle erosion

Fig. 23 shows the event tree, the unreliability functions

and risk functions of nozzle erosion event. Nozzle erosion

event [A] shows relatively high unreliability but low cost for

recovery action. The risk function is relatively low. For

other events [B], [C], [D], lower unreliability and high

recovery cost result in the same level risk as event [A].

The total risk is increasing function of operation period.

Here, operation period is taken as operation hours but

number of starts also applicable.

Fig. 24 shows scenario case study for nozzle erosion

events and maintenance action. The scenario 1 comprises

predetermined replace period without preventive repair

action during the service period. It requires no maintenance

cost but runs high risk. The scenario 2 comprises

predetermined replace period with the scheduled preventive

repair actions during the service period. As the repair cost is

relatively low in this case, the sum of risk and cumulative

preventive costs remains low level. The scenario 3

comprises early replacement of erosion resistant upgraded

nozzle and long term use of the upgraded one. It arises

higher cost in the early period but relatively lower

increase in the total cost for long period. The comparison

of total cost gives the optimum maintenance scenario for the

same period.


7. Concluding remarks

The quantitative RBM method for steam turbines was

presented. Statistical formulation of failure probability as

the function of time or cycles were very effective way to

estimate the risk for various modes of failure and the chain

of successive failure. The RBM system has various features

as follows

(1)
Describe plant maintenance scenario by component
breakdown trees and life cycle event trees.

(2)
Assign default unreliability function to every event by
statistical analysis of master filed failure database.

(3)
Customize the unreliability functions can be modified
reflecting the inspection information and probabilistic

life assessment.

(4)
Optimize maintenance intervals using risk functions,
cost functions and income functions.

(5)
Optimize maintenance scenario using life cycle event
trees and total cost analysis.

Those features are useful for wider application,

not restricted to the steam turbine plant. The application

of risk-based engineering is shown in Fig. 25. The

quantitative method provides feedback and optimization

of design, manufacturing and operation parameters. Those

approaches will lead more economical and reliable plant

design, manufacturing and management of plants.

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Risk Based Inspection and Maintenance Systems for Steam Turbines

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