The performance of gas turbines degrades over time and, as a consequence, a decrease in gas turbine performance parameters also occurs, so that they may fall below a given threshold value. Therefore, corrective maintenance actions are required to bring the system back to an acceptable operating condition. In today’s competitive market, the prognosis of the time evolution of system performance is also recommended, in such a manner as to take appropriate action before any serious malfunctioning has occurred and, as a consequence, to improve system reliability and availability. Successful prognostics should be as accurate as possible, because false alarms cause unnecessary maintenance and nonprofitable stops. For these reasons, a prognostic methodology, developed by the authors, is applied in this paper to assess its prediction reliability for several degradation scenarios typical of gas turbine performance deterioration. The methodology makes use of the Monte Carlo statistical method to provide, on the basis of the recordings of past behavior, a prediction of future availability, i.e., the probability that the considered machine or component can be found in the operational state at a given time in the future. The analyses carried out in this paper aim to assess the influence of the degradation scenario on methodology prediction reliability, as a function of a user-defined threshold and minimum value allowed for the parameter under consideration. A technique is also presented and discussed, in order to improve methodology prediction reliability by means a correction factor applied to the time points used for methodology calibration. The results presented in this paper show that, for all the considered degradation scenarios, the prediction error is lower than 4% (in most cases, it is even lower than 2%), if the availability is estimated for the next trend, while it is not higher than 12%, if the availability is estimated five trends ahead. The application of a proper correction factor allows the prediction errors after five trends to be reduced to approximately 5%.

References

References
1.
Kestner
,
B. K.
,
Lee
,
Y. K.
,
Voleti
,
G.
,
Mavris
,
D. N.
,
Kumar
,
V.
, and
Lin
,
T.
, 2011, “
Diagnostics of Highly Degraded Industrial Gas Turbines Using Bayesian Networks
,” ASME Paper No. GT2011-46537.
2.
Diallo
,
O.
, and
Mavris
,
D.
, 2011, “
A Data Analytics Approach to Failure Precursor Detection of Gas Turbine
,” ASME Paper No. GT2011-46019.
3.
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
.
4.
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.
5.
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. GT2002-30248.
6.
Doel
,
D. L.
, 2003, “
Development of Baselines, Influence Coefficients and Statistical Inputs for Gas Path Analysis
,” Gas Turbine Monitoring & Fault Diagnosis (von Karman Institute Lecture Series 2003-11), Jan. 13–17.
7.
Li
,
Y. G.
, 2004, “
Gas Turbine Diagnosis Using a Fault Isolation Enhanced GPA
,” ASME Paper No. GT2004-53571.
8.
Jaw
,
L. C.
, 2005, “
Recent Advancements in Aircraft Engine Health Management (EHM) Technologies and Recommendations for the Next Step
,” ASME Paper No. GT2005-68625.
9.
Verbist
,
M. L.
,
Visser
,
W. P. J.
,
van Buijtenen
,
J. P.
, and
Duivis
,
R.
, 2011, “
Gas Path Analysis on KLM In-Flight Engine Data
,” ASME Paper No. GT2011-45625.
10.
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.
11.
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.
12.
Puggina
,
N.
, and
Venturini
,
M.
, 2012, “
Development of a Statistical Methodology for Gas Turbine Prognostics
,”
ASME J. Eng. Gas Turbines Power
,
134
(
2
), p.
022401
.
13.
Watson
,
M. J.
,
Smith
,
M. J.
,
Kloda
,
J.
,
Byington
,
C. S.
, and
Semega
,
K.
, 2011, “
Prognostics and Health Management of Aircraft Engine EMA Systems
,” ASME Paper No. GT2011-46537.
14.
Palmé
,
T.
,
Breuhaus
,
P.
,
Assadi
,
M.
,
Klein
,
A.
, and
Kim
,
M.
, 2011, “
Early Warning of Gas Turbine Failure by Nonlinear Feature Extraction Using an Auto-Associative Neural Network Approach
,” ASME Paper No. GT2011-45991.
15.
Li
,
Y. G.
, and
Nilkitsaranont
,
P.
, 2009, “
Gas Turbine Performance Prognostic for Condition-Based Maintenance
,”
Appl. Eng.
,
86
, pp.
2152
2161
.
16.
Borguet
,
S.
, and
Leonard
,
O.
, 2008, “
A Generalised Likelihood Ratio Test for Adaptive Gas Turbine Health Monitoring
,” ASME Paper No. GT2008-50117.
17.
Hepperle
,
N.
,
Therkorn
,
D.
,
Schneider
,
E.
, and
Staudacher
,
S.
, 2011, “
Assessment of Gas Turbine and Combined Cycle Power Plant Performance Degradation
,” ASME Paper No. GT2011-45375.
18.
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.
19.
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.
20.
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.
21.
Kurz
,
R.
, and
Brun
,
K.
, 2011, “
Fouling Mechanisms in Axial Compressors
,” ASME Paper No. GT2011-45012.
22.
Igie
,
U.
,
Pilidis
,
P.
,
Fouflias
,
D.
,
Ramsden
,
K.
, and
Lambart
,
P.
, 2011, “
On-Line Compressor Cascade Washing for Gas Turbine Performance Investigation
,” ASME Paper No. GT2011-46210.
23.
Fabbri
,
A.
,
Traverso
,
A.
, and
Cafaro
,
S.
, 2011, “
Compressor Performance Recovery Systems: A New Thermoeconomic Approach
,” ASME Paper No. GT2011-45121.
24.
Lipowsky
,
H.
,
Staudacher
,
S.
,
Bauer
,
M.
, and
Schmidt
,
K. J.
, 2009, “
Application of Bayesian Forecasting to Change Detection and Prognosis of Gas Turbine Performance
,” ASME Paper No. GT2009-59447.
25.
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.
26.
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.
27.
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
.
28.
Palmé
,
T.
,
Breuhaus
,
P.
,
Assadi
,
M.
,
Klein
,
A.
, and
Kim
,
M.
, 2011, “
New Alstom Monitoring Tools Leveraging Artificial Neural Networks Technologies
,” ASME Paper No. GT2011-45990.
29.
Fishman
,
G. S.
, 1996,
Monte Carlo: Concepts, Algorithms and Applications
,
Springer-Verlag
,
New York
.
30.
Dubi
,
A.
, 2000,
Monte Carlo Applications in Systems Engineering
,
Wiley
,
New York
.
31.
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.
32.
Pinelli
,
M.
, and
Venturini
,
M.
, 2001, “
Operating State Historical Data Analysis to Support Gas Turbine Malfunction Detection
,” ASME Paper No. IMECE2001/AES-23665.
33.
Wilcox
,
M.
, and
Brun
,
K.
, 2011, “
Gas Turbine Inlet Filtration System Life Cycle Cost Analysis
,” ASME Paper No. GT2011-46708.
34.
Spieler
,
S.
,
Staudacher
,
S.
,
Fiola
,
R.
,
Sahm
,
P.
, and
Weisschuh
,
M.
, 2007, “
Probabilistic Engine Performance Scatter and Deterioration Modeling
,” ASME Paper No. GT2007-27051.
35.
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.
36.
Larsson
,
E.
,
Aslund
,
J.
,
Frisk
,
E.
, and
Eriksson
,
L.
2011, “
Health Monitoring in an Industrial Gas Turbine Application by Using Model Based Diagnosis Techniques
,” ASME Paper No. GT2011-46825.
You do not currently have access to this content.