To optimize both production and maintenance, from both a technical and an economical point of view, it would be advisable to predict the future health condition of a system and of its components, starting from field measurements taken in the past. For this purpose, this paper presents a methodology, based on the Monte Carlo statistical method, which aims to determine the future operating state of a gas turbine. The methodology allows the system future availability to be estimated, to support a prognostic process based on past historical data trends. One of the most innovative features is that the prognostic methodology can be applied to both global and local performance parameters, as, for instance, machine specific fuel consumption or local temperatures. First, the theoretical background for developing the prognostic methodology is outlined. Then, the procedure for implementing the methodology is developed and a simulation model is set up. Finally, different degradation-over-time scenarios for a gas turbine are simulated and a sensitivity analysis on methodology response is carried out, to assess the capability and the reliability of the prognostic methodology. The methodology proves robust and reliable, with a prediction error lower than 2%, for the availability associated with the next future data trend.

References

References
1.
Hoeft
,
R. F.
, 1996, “
Heavy Duty Gas Turbine Operating & Maintenance Considerations
,”
Proceedings of the 39th Ge Turbine State-of-the-Art Technology Seminar
, Ge Paper No. GER-3620d.
2.
Bettocchi
,
R.
,
Pinelli
,
M.
,
Spina
,
P. R.
,
Venturini
,
M.
, and
Sebastianelli
,
S.
, 2001, “
A System for Health State Determination of Natural Gas Compression Gas Turbines
,” ASME Paper No. 2001-GT-223.
3.
Therkorn
,
D.
, 2005, “
Remote Monitoring and Diagnostic for Combined-Cycle Power Plants
,” ASME Paper No. GT2005-68710.
4.
DePold
,
H. R.
, and
Siegel
,
J.
, 2006, “
Using Diagnostics and Prognostics to Minimize the Cost of Ownership of Gas Turbines
,” ASME Paper No. GT2006-91183.
5.
Stamatis
,
A.
,
Mathioudakis
,
K.
, and
Papailiou
,
K. D.
, 1990, “
Adaptive Simulation of Gas Turbine Performance
,”
ASME J. Eng. Gas Turbines Power
,
112
, pp.
168
175
.
6.
Bettocchi
,
R.
, and
Spina
,
P. R.
, 1999, “
Diagnosis of Gas Turbine Operating Conditions by Means of the Inverse Cycle Calculation
,” ASME Paper No. 99-GT-185.
7.
Pinelli
,
M.
, and
Venturini
,
M.
, 2002, “
Application of Methodologies to Evaluate the Health State of Gas Turbines in a Cogenerative Combined Cycle Power Plant
,” ASME Paper No. GT-2002-30248.
8.
Doel
,
D. L.
, 2003, “
Development of Baselines, Influence Coefficients and Statistical Inputs for Gas Path Analysis
,”
Gas Turbine Monitoring and Fault Diagnosis
(von Karman Institute Lecture Series 2003-1), Jan 13–17.
9.
Li
,
Y. G.
, 2004, “
Gas Turbine Diagnosis Using a Fault Isolation Enhanced GPA
,” ASME Paper No. GT2004-53571.
10.
Jaw
,
L. C.
, 2005, “
Recent Advancements in Aircraft Engine Health Management (EHM) Technologies and Recommendations for the Next Step
,” ASME Paper No. GT2005-68625.
11.
Hindle
,
E.
,
Van Stone
,
R.
,
Brogan
,
C.
,
Ken Dale
,
J. V.
, and
Gibson
,
N.
, 2006, “
A Prognostic and Diagnostic Approach to Engine Health Management
,” ASME Paper No. GT2006-90614.
12.
Roemer
,
M. J.
,
Byington
,
C. S.
,
Kacprzynski
,
G. J.
, and
Vachtsevanos
,
G.
, 2006, “
An Overview of Selected Prognostic Technologies with Application to Engine Health Management
,” ASME Paper No. GT2006-90677.
13.
Li
,
Y. G.
, and
Nilkitsaranont
,
P.
, 2009, “
Gas Turbine Performance Prognostic for Condition-Based Maintenance
,”
Appl. Energy
,
86
, pp.
2152
2161
.
14.
Borguet
,
S.
, and
Leonard
,
O.
, 2008, “
A generalized Likelihood Ratio Test for Adaptive Gas Turbine Health Monitoring
,” ASME Paper No. GT2008-50117.
15.
Tarabrin
,
A. P.
,
Bodrov
,
A. I.
,
Schurovsky
,
V. A.
, and
Stalder
,
J -P.
, 1998, “
Influence of Axial Compressor Fouling on Gas Turbine Unit Performance Based on Different Schemes and with Different Initial Parameters
,” ASME Paper No. 98-GT-416.
16.
Meher-Homji
,
C. B.
,
Chaker
,
M.
, and
Bromley
,
A. F.
, 2009, “
The Fouling of Axial Flow Compressors – Causes, Effects, Susceptibility End Sensitivity
,” ASME Paper No. GT2009-59239.
17.
Schneider
,
E.
,
Demirciogiu
,
S.
,
Franco
,
S.
, and
Therkorn
,
D.
, 2009, “
Analysis of Compressor On-Line Washing to Optimize Gas Turbine Power Plant Performance
,” ASME Paper No. GT2009-59356.
18.
Lipowsky
,
H.
,
Staudacher
,
S.
,
Bauer
,
M.
, and
Schmidt
,
K. J.
, 2010, “
Application of Bayesian Forecasting to Change Detection and Prognosis of Gas Turbine Performance
,”
ASME J. Eng. Gas Turbines Power
,
132
(
3
), p.
031602
.
19.
Zaluski
,
M.
,
Letourneau
,
S.
,
Bird
,
J.
, and
Yang
,
C.
, 2010, “
Developing Data Mining-Based Prognostic Models for CF-18 Aircraft
,” ASME Paper No. GT2010-22944.
20.
Bryg
,
D. J.
,
Mink
,
G.
, and
Jaw
,
L. C.
, 2008, “
Combining Lead Functions and Logistic Regression for Predicting Failures on an Aircraft Engine
,” ASME Paper No. GT2008-50118.
21.
Fishman
,
G. S.
, 1996,
Monte Carlo: Concepts, Algorithms and Applications
,
Springer
,
Berlin
.
22.
Dubi
,
A.
, 2000,
Monte Carlo Applications in Systems Engineering
,
Wiley
,
New York
.
23.
Spieler
,
S.
,
Staudacher
,
S.
,
Fiola
,
R.
,
Sahm
,
P.
, and
Weisschuh
,
M.
, 2007, “
Probabilistic Engine Performance Scatter and Deterioration Modeling
,” ASME Paper No. GT2007-27051.
24.
Muller
,
M.
,
Staudacher
,
S.
,
Friedl
,
W. H.
,
Kohler
,
R.
, and
Weisschuh
,
M.
, 2010, “
Probabilistic Engine Maintenance Modeling for Varying Environmental and Operating Conditions
,” ASME Paper No. GT2010-22548.
25.
Sekhon
,
R.
,
Bassily
,
H.
, and
Wagner
,
J.
, 2008, “
A Comparison of Two Trending Strategies for Gas Turbine Performance Prediction
,”
ASME J. Eng. Gas Turbines Power
,
130
(
7
), p.
041601
.
26.
Cavarzere
,
A.
, and
Venturini
,
M.
, 2012, “
Application of Forecasting Methodologies to Predict Gas Turbine Behavior Over Time
,”
ASME J. Eng. Gas Turbines Power
,
134
(
1
), p.
012401
.
27.
Pinelli
,
M.
, and
Venturini
,
M.
, 2001, “
Operating State Historical Data Analysis to Support Gas Turbine Malfunction Detection
,” ASME IMECE(2001)/AES-23665.
28.
Davison
,
C.
, and
Drummond
,
C.
, 2009, “
Application of Cost Matrices and Cost Curves to Enhance Diagnostic Health Management Metrics for Gas Turbine Performance
,” ASME Paper No. GT2009-59630.
29.
Doel
,
D. L.
, 2003, “
A Weighted-Least-Squares Gas Path Analysis Method for Test Cell or On-Wing Data
,”
Gas Turbine Monitoring and Fault Diagnosis
(von Karman Institute Lecture Series 2003-01), Jan 13–17.
30.
Spina
,
P. R.
, 2000, “
Reliability in the Determination of Gas Turbine Operating State
,”
Proceedings of the 39th IEEE Conference on Decision and Control
,
Sydney, Australia
, Paper No. CDC00-INV5805.
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