Warranty and Maintenance Decision Making for Gas Turbines Susan Y. Chao*, Zu-Hsu Lee, and Alice M....
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Transcript of Warranty and Maintenance Decision Making for Gas Turbines Susan Y. Chao*, Zu-Hsu Lee, and Alice M....
Warranty and Maintenance Decision Making for Gas Turbines
Susan Y. Chao*, Zu-Hsu Lee†, and Alice M. Agogino‡
University of California, Berkeley
Berkeley, CA 94720
Acknowledgments Many thanks to General Electric
Corporate Research and Development and the University of California MICRO Program.
Special thanks to Louis Schick and Mahesh Morjaria of General Electric Corporate Research and Development for their guidance and intellectual input.
Gas Turbine Basics Complex system: large number of
parts subject to performance degradation, malfunction, or failure.
Turbine, combustion system, hot-gas path equipment, control devices, fuel metering, etc.
Condition information available from operators, sensors, inspections.
Gas Turbine Maintenance Enormous number of candidates for
maintenance, so ideally focus on most cost-effective items.
Maintenance planning (optimized, heuristic, ad hoc) determines: Inspection activities Maintenance activities Intervals between inspection and
maintenance activities.
On-line Statistical AnalysisExpert Subjective ProbabilitiesOn-line Machine LearningKnowledge ExtractionDiagnosis
Maintenance Planning
Sensor Fusion
Sensor Validation
MaintenancePlanning
Repair or Replace PartsOrder Inspections
Sensor ReadingsInspection Results
Gas Turbine Warranty Warranty/service contract for gas
turbine would transfer all necessary maintenance and repair responsibilities to the manufacturer for the life of the warranty.
Fixed warranty period determined by manufacturer.
Gas turbine customer pays fixed price for warranty.
4 Key Issues Types of maintenance and sensing
activities (current focus) Price of a gas turbine and service
contract Length of service contract period Number of gas turbines for consumer
Consumer Profit MaximizationHow many gas turbines should the
customer purchase, if any?
Maximize Rj (nj,w)–(p1 + p2) *nj* -
n (w/* shutdown loss
Producer Profit MaximizationHow much should the manufacturer
charge for a gas turbine engine and warranty?
How long should the warranty period be?
Maximize (p1 + p2 - m) *nj*
p1,p2,w
Subject To m=F0 (xt, s, ts) .
Optimal MaintenanceWhat types of maintenance and sensing
activities should the manufacturer pursue? How often?
Derive an optimal maintenance policy via stochastic dynamic programming to minimize maintenance costs, given a fixed warranty period.
Solve for F0 (xt, s, ts).
Gas Turbine Water Wash Maintenance Focus on a specific area of gas turbine
maintenance: compressor water washing.
Compressor degradation results from contaminants (moisture, oil, dirt, etc.), erosion, and blade damage.
Maintenance activities scheduled to minimize expected maintenance cost while incurring minimum profit loss caused by efficiency degradation.
Compressor Efficiency Motivation: if fuel is 3¢/KWHr, then 1%
loss of efficiency on a 100MW turbine = $30/hr or $263K/yr.
On-line washing with or without detergents (previously nutshells) relatively inexpensive; can improve efficiency ~1%.
Off-line washing more expensive, time consuming; can improve efficiency ~2-3%.
Decision Alternatives
Blade replacement
Major scouring
Do nothing
On-line wash
Do nothing
Off-line wash
Major inspection
Influence Diagram
CurrentEngineState, s´
AverageEfficiency,
xt
Decision,d
TotalMaintenance
Cost, v
LastMeasured
EngineState, s
Stochastic Dynamic Programming Computes minimum expected costs
backwards, period by period. Final solution gives expected
minimum maintenance cost, which can be used to determine appropriate warranty price.
Given engine status information for any period, model chooses optimal decision for that period.
Stochastic Dynamic Programming Assumptions Problem divided into periods, each ending
with a decision. Finite number of possible states associated
with each period. Decision and engine state for any period
determine likelihood of transition to next state.
Given current state, optimal decision for subsequent states does not depend on previous decisions or states.
Other Assumptions Compressor working performance is main
determinant of engine efficiency level. Working efficiency and engine state can
be represented as discrete variables. Current efficiency can be derived from
temperature and pressure statistics. Intra-period efficiency transition
probability depends on maintenance decision and engine state.
Dynamic Program Constraints
c c d P x x s d
P s s t t loss x F x s t
t txs
s t t t s
t1 1 1 1
1 1 1
1
( ) , , )
, ( ) ( , , )
c c d P x x s d
P s s t t loss x F x s t
t txs
s t t t s
t2 2 1 2
1 1 1
1
( ) , , )
, ( ) ( , , )
Dynamic Program Constraints
c c d P s s t t
c dP x x s d
loss x F x s t
ss
d d d d
t t
t t t sx t
3 3
1
1 1 14 5 6 1
( ) ,
min ( ), , )
( ) ( , , ), ,
cP x x s d P s s t t
loss x F x s t
t t s
t t t sxs t
7
1 7
1 1 11
, , ) ,
( ) ( , , )
Dynamic Program Constraints
c c d P s s t t
c dP x x s d
loss x F x s t
ss
d d d d
t t
t t t sx t
3 3
1
1 1 14 5 6 1
( ) ,
min ( ), , )
( ) ( , , ), ,
Ft (xt, s, ts) = min [ c1, c2, c3, c7 ]
cP x x s d P s s t t
loss x F x s t
t t s
t t t sxs t
7
1 7
1 1 11
, , ) ,
( ) ( , , )
Dynamic Program SimulationUser/Other Inputs
Service Contract period Cost of each decision Losses incurred at each
efficiency level Transition probabilities
for state and efficiency changes
Program Outputs
Expected minimum maintenance cost
Optimal action for any period
Turbine Performance Degradation Curves*
*Source: GE
Turbine Performance Degradation Curves*
*Source: GE
Online Water Wash Effects*
*Source: GE
indep1 etac l4ww shown
y = -0.0132x + 562.92
86
86.5
87
87.5
88
88.5
89
89.5
90
8/31/98 0:00 9/5/98 0:00 9/10/98 0:00 9/15/98 0:00 9/20/98 0:000.1
Online Water Wash Effects*
*Source: GE
indep1 flow l4ww shown
800
820
840
860
880
900
920
940
8/31/98 0:00 9/5/98 0:00 9/10/98 0:00 9/15/98 0:00 9/20/98 0:00 9/25/98 0:00
0.1
Efficiency Transition Probabilities
X(t+1)=
1 2 3 4
X(t)=1 >0;
1,2,4,5,6,7
>0;6,7
0 0
2 >0;1,2,4,5
>0;1,2,4,6,7
>0;6,7
0
3 >0;4,5
>0;1,2,4
>0;1,2,4,6,7
>0;6,7
4 >0;4,5
>0;4
>0;1,2,4
>0;1,2,4,6,7
Conclusions Analyzed maintenance and warranty
decision making for gas turbines used in power plants.
Described and modeled economic issues related to warranty.
Developed a dynamic programming approach to optimize maintenance activities and warranty period length suited in particular to compressor maintenance.
Future Research Sensitivity analysis of all user-input
costs . Sensitivity analysis of the efficiency
and state transition probabilities.