In the paper, neural network (NN) models for gas turbine diagnostics are studied and developed. The analyses carried out are aimed at the selection of the most appropriate NN structure for gas turbine diagnostics, in terms of computational time of the NN training phase, accuracy, and robustness with respect to measurement uncertainty. In particular, feed-forward NNs with a single hidden layer trained by using a back-propagation learning algorithm are considered and tested. Moreover, multi-input/multioutput NN architectures (i.e., NNs calculating all the system outputs) are compared to multi-input/single-output NNs, each of them calculating a single output of the system. The results obtained show that NNs are sufficiently robust with respect to measurement uncertainty, if a sufficient number of training patterns are used. Moreover, multi-input/multioutput NNs trained with data corrupted with measurement errors seem to be the best compromise between the computational time required for NN training phase and the NN accuracy in performing gas turbine diagnostics.

1.
Hoeft
,
R. F.
, 1996, “
Heavy Duty Gas Turbine Operating & Maintenance Considerations
,”
Proc. of 39th GE Turbine State-of-the-Art Technology Seminar
, GE ed., Schenectady, NY, August 26–29, Paper No. GER-3620D.
2.
Rajamani
,
R.
,
Wang
,
J.
, and
Jeong
,
K.-Y.
, 2004, “
Condition-Based Maintenance for Aircraft Engines
,” ASME Paper No. GT2004-54127.
3.
Meher-Homji
,
B. C.
, and
Cullen
,
P. J.
, 1992, “
Integration of Condition Monitoring Technologies for the Health Monitoring of Gas Turbines
,” ASME Paper No. 92-GT-52.
4.
Meher-Homji
,
B. C.
,
Boyce
,
P. M.
,
Lakshminarashima
,
A. N.
,
Whitten
,
A. J.
, and
Meher-Homji
,
J. F.
, 1993, “
Condition Monitoring and Diagnostic Approaches for Advanced Gas Turbines
,”
Proc. of 7th ASME COGEN-TURBO
, Bournemouth, UK, IGTI Vol.
8
, pp.
347
354
.
5.
Madej
,
J.
,
Longtin
,
K.
, and
Smith
,
D. P.
, 1996, “
Monitoring Service Delivery System and Diagnostics
,”
Proc. of 39th GE Turbine State-of-the-Art Technology Seminar
, GE ed., Schenectady, NY, August 26–29, Paper No. GER-3956.
6.
Tsalavoutas
,
A.
,
Aretakis
,
N.
,
Mathioudakis
,
K.
, and
Stamatis
,
A.
, 2000, “
Combining Advanced Data Analysis Methods for the Constitution of an Integrated Gas Turbine Condition Monitoring and Diagnostic System
,” ASME Paper No. 2000-GT-0034.
7.
Bettocchi
,
R.
,
Pinelli
,
M.
,
Spina
,
P. R.
,
Venturini
,
M.
, and
Sebastanelli
,
S.
, 2001, “
A System for Health State Determination of Natural Gas Compression Gas Turbines
,” ASME Paper No. 2001-GT-0223.
8.
Veer
,
T.
,
Ulvestad
,
A.
, and
Bolland
,
O.
, 2004, “
Frame, a Toll for Predicting Gas Turbine Condition as Well as Reliability, Availability Performance
,” ASME Paper No. GT2004-53770.
9.
Byington
,
C. S.
,
Roemer
,
M. J.
,
Watson
,
M. J.
,
Galie
,
T. R.
, and
Savage
,
C.
, 2004, “
Prognostic Enhancement to Diagnostic Systems (PEDS) Applied to Shipboard Power Generation Systems
,” ASME Paper No. GT2004-54135.
10.
Li
,
Y. G.
, 2002, “
Performance Analysis Based Gas Turbine Diagnostics: A Review
,”
Proc. Inst. Mech. Eng., Part A
0957-6509,
216
, pp.
363
377
.
11.
Urban
,
L. A.
, 1972, “
Gas Path Analysis Applied to Turbine Engine Condition Monitoring
,”
Proc. of AIAA/SAE 8th Joint Propulsion Conference
, New Orleans,
AIAA
, Washigton, DC, AIAA Paper No. 72-1082.
12.
Stamatis
,
A.
, and
Papailiou
,
K. D.
, 1988, “
Discrete Operating Conditions Gas Path Analysis
,”
Proceedings, AGARD Conference
,
Quebec, Canada, pp.
33
-1–33-
10
.
13.
Urban
,
L. A.
, and
Volponi
,
A. J.
, 1992, “
Mathematical Methods of Relative Engine Performance Diagnostics
,”
SAE Trans.
0096-736X,
101
, p.
922048
.
14.
Doel
,
D. L.
, 1994, “
TEMPER—A Gas-Path Analysis Tool for Commercial Jet Engines
,”
ASME J. Eng. Gas Turbines Power
0742-4795,
116
, pp.
82
89
.
15.
Doel
,
D. L.
, 1994, “
An Assessment of Weighted-Least-Squares Based Gas Path Analysis
,”
ASME J. Eng. Gas Turbines Power
0742-4795,
116
, pp.
366
373
.
16.
Stamatis
,
A.
,
Mathioudakis
,
K.
, and
Papailiou
,
K. D.
, 1990, “
Adaptive Simulation of Gas Turbine Performance
,”
ASME J. Eng. Gas Turbines Power
0742-4795,
112
, pp.
168
175
.
17.
Benvenuti
,
E.
,
Bettocchi
,
R.
,
Cantore
,
G.
,
Negri di Montenegro
,
G.
, and
Spina
,
P. R.
, 1993, “
Gas Turbine Cycle Modeling Oriented to Component Performance Evaluation from Limited Design or Test Data
,”
Proc. of 7th ASME COGEN-TURBO
, Bournemouth, UK,
ASME
, New York, IGTI Vol.
8
, pp.
327
337
.
18.
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.
19.
Pinelli
,
M.
,
Spina
,
P. R.
, and
Venturini
,
M.
, 2003, “
Optimized Operating Point Selection for Gas Turbine Health State Analysis by Using a Multi-Point Technique
,” ASME Paper No. GT2003-38191.
20.
Jang
,
J.-S. R.
,
Sun
,
C.-T.
, and
Mizutani
,
E.
, 1997,
Neuro-Fuzzy and Soft Computing
,
Prentice-Hall
, Englewood Cliffs, NJ.
21.
Torella
,
G.
, and
Lombardo
,
G.
, 1996, “
Neural Networks for the Diagnostics of Gas Turbine Engines
,” ASME Paper No. 96-TA-39.
22.
Kanelopoulos
,
K.
,
Stamatis
,
A.
, and
Mathioudakis
,
K.
, 1997, “
Incorporating Neural Networks Into Gas Turbine Performance Diagnostics
,” ASME Paper No. 97-GT-35.
23.
Volponi
,
A. J.
,
DePold
,
H. R.
,
Ganguli
,
R.
, and
Daguang
,
C.
, 2000, “
The Use of Kalman Filter and Neural Networks Methodologies in Gas Turbine Performance Diagnostics: A Comparative Study
,” ASME Paper No. 2000-GT-0547.
24.
Romessis
,
C.
,
Stamatis
,
A.
, and
Mathioudakis
,
K.
, 2001, “
A Parametric Investigation of the Diagnostic Ability of Probabilistic Neural Networks on Turbofan Engines
,” ASME Paper No. 2001-GT-0011.
25.
Bettocchi
,
R.
,
Spina
,
P. R.
, and
Torella
,
G.
, 2002, “
Gas Turbine Health Indices Determination by Using Neural Networks
,” ASME Paper No. GT-2002-30276.
26.
Arriagada
,
J.
,
Genrup
,
M.
,
Loberg
,
A.
, and
Assadi
,
M.
, 2003, “
Fault Diagnosis System for an Industrial Gas Turbine by Means of Neural Networks
,”
Proc. International Gas Turbine Congress 2003 (IGTC’03)
, Tokyo, Nov. 2–7,
GTSJ
,
Tokyo
, Paper No. IGTC2003, Tokyo TS-001.
27.
Sampath
,
S.
, and
Singh
,
R.
, 2004, “
An Integrated Fault Diagnostics Model Using Genetic Algorithm and Neural Networks
,” ASME Paper No. GT-2004-53914.
28.
Ganguli
,
R.
, 2003, “
Application of Fuzzy Logic for Fault Isolation of Jet Engines
,”
ASME J. Eng. Gas Turbines Power
0742-4795,
125
, pp.
617
623
.
29.
Ganguli
,
R.
,
Verma
,
R.
, and
Roy
,
N.
, 2004, “
Soft Computing Application for Gas path Fault Isolation
,” ASME Paper No. GT-2004-53209.
30.
Console
,
L.
, and
Torasso
,
P.
, 1989,
Diagnostic Problem Solving: Combining Heuristic, Approximate and Causal Reasoning
,
Van Nostrand Reinhold
,
New York
.
31.
Doel
,
D. L.
, 1990, “
The Role of Expert Systems in Commercial Gas Turbine Engine Monitoring
,” ASME Paper No. 90-GT-374.
32.
Torella
,
G.
, 1992, “
Expert Systems for the Trouble-Shooting and the Diagnostics of Engines
,” AIAA Paper No. 92-3327.
33.
Palmer
,
C. A.
, 1998, “
Combining Bayesian Belief Networks With Gas Path Analysis for Test Cell Diagnostics and Overhaul
,” ASME Paper No. 98-GT-168.
34.
DePold
,
H. R.
, and
Gass
,
F. D.
, 1999, “
The Application of Expert Systems and Neural Networks to Gas Turbine Prognostics and Diagnostics
,”
ASME J. Eng. Gas Turbines Power
0742-4795,
121
, pp.
607
612
.
35.
Spina
,
P. R.
,
Torella
,
G.
, and
Venturini
,
M.
, 2002, “
The Use of Expert Systems for Gas Turbine Diagnostics and Maintenance
,” ASME Paper No. GT-2002-30033.
36.
Jain
,
L. C.
, and
Martin
,
N. M.
, 1998,
Fusion of Neural Networks, Fuzzy Sets, and Genetic Algorithms
,
CRC Press
, Boca Raton.
37.
Bettocchi
,
R.
,
Pinelli
,
M.
,
Venturini
,
M.
,
Spina
,
P. R.
,
Bellagamba
,
S.
, and
Tirone
,
G.
, 2002, “
Procedura di Calibrazione del Programma per la Diagnosi Funzionale dei Turbogas della Centrale a Ciclo Combinato di La Spezia
,”
Proc. 57th Congresso Nazionale ATI
, Pisa, Italy, ETS ed., September 17–20, pp.
III
-B-3–III-B-
12
(in Italian).
38.
Stamatis
,
A.
,
Mathioudakis
,
K.
, and
Papailiou
,
K.
, 1992, “
Optimal Measurement and Health Index Selection for Gas Turbine Performance Status and Fault Diagnosis
,”
ASME J. Eng. Gas Turbines Power
0742-4795,
114
, pp.
209
216
.
39.
Bettocchi
,
R.
,
Spina
,
P. R.
, and
Benvenuti
,
E.
, 2000, “
Set-Up of an Adaptive Method for the Diagnosis of Gas Turbine Operating State by Using Test-Bench Measurements
,” ASME Paper No. 2000-GT-0309.
40.
Mathioudakis
,
K.
, and
Tsalavoutas
,
A.
, 2001, “
Uncertainty Reduction in Gas Turbine Performance Diagnostics by Accounting for Humidity Effects
,” ASME Paper No. 2001-GT-0010.
41.
Pinelli
,
M.
, and
Spina
,
P. R.
, 2002, “
Gas Turbine Field Performance Determination: Sources of Uncertainties
,”
ASME J. Eng. Gas Turbines Power
0742-4795,
124
, pp.
155
160
.
42.
Mathioudakis
,
K.
, 2003, “
Non-linear Methods for Gas Turbine Fault Diagnostics
,”
Von Karman Institute Lecture Series 2003-01
, Gas Turbine Monitoring & Fault Diagnosis, Jan. 13–17.
43.
Mathioudakis
,
K.
,
Kamboukos
,
Ph.
, and
Stamatis
,
A.
, 2002, “
Turbofan Performance Deterioration Tracking Using Non-linear Models and Optimization Techniques
,” ASME Paper No. GT-2002-30026.
44.
Mathioudakis
,
K.
, and
Kamboukos
,
Ph.
, 2004, “
Assessment of the Effectiveness of Gas Path Diagnostic Schemes
,” ASME Paper No. GT-2004-53862.
45.
Haykin
,
S.
, 1999,
Neural Networks—A Comprehensive Foundation
,
2nd ed.
,
Prentice-Hall
, Englewood Cliffs, NJ.
46.
Cybenko
,
G.
, 1989, “
Approximation by Superimposition of a Sigmoidal Function
,”
Math. Control, Signals, Syst.
0932-4194,
2
, pp.
303
314
.
47.
Rumenlhart
,
D. E.
,
Hinton
,
G. E.
, and
Williams
,
R. J.
, 1986, “
Learning Internal Representation by Error Propagation, Parallel Distributed Processing
,”
Explor. Microstruct. Cognition
,
1
, pp.
318
362
.
48.
Bettocchi
,
R.
,
Pinelli
,
M.
,
Spina
,
P. R.
, and
Venturini
,
M.
, 2003,
Statistical Analyses to Improve Gas Turbine Diagnostics Reliability
,
Proc. of 8th International Gas Turbine Congress
, 2003 (IGTC'03), Tokyo, November 2–7 GTSJ, Tokyo, Paper No. IGTC2003Tokyo TS-004.
49.
Pinelli
,
M.
, and
Venturini
,
M.
, 2002, “
Application of Methodologies to Evaluate the Health State of Gas Turbines in a Cogenerative Combined Power Plant
,” ASME Paper No. GT-2002-30248.
50.
Bettocchi
,
R.
,
Pinelli
,
M.
,
Spina
,
P. R.
,
Venturini
,
M.
, and
Burgio
,
M.
, 2004, “
Set Up of a Robust Neural Network for Gas Turbine Simulation
,” ASME Paper No. GT2004-53421.
You do not currently have access to this content.