This paper presents the development of an integrated fault diagnostics model for identifying shifts in component performance and sensor faults using the Genetic Algorithm and Artificial Neural Network. The diagnostics model operates in two distinct stages. The first stage uses response surfaces for computing objective functions to increase the exploration potential of the search space while easing the computational burden. The second stage uses the concept of a hybrid diagnostics model in which a nested neural network is used with genetic algorithm to form a hybrid diagnostics model. The nested neural network functions as a pre-processor or filter to reduce the number of fault classes to be explored by the genetic algorithm based diagnostics model. The hybrid model improves the accuracy, reliability, and consistency of the results obtained. In addition significant improvements in the total run time have also been observed. The advanced cycle Intercooled Recuperated WR21 engine has been used as the test engine for implementing the diagnostics model.

1.
Doel
,
D. L.
, 1994, “
TEMPER: Gas-Path Analysis Tool for Commercial Jet Engines
,”
ASME J. Eng. Gas Turbines Power
0742-4795,
116
(
1
), pp.
82
89
.
2.
Doel
,
D.
, 2002, “
Interpretation of Weighted-Least-Squares Gas Path Analysis Results
,” ASME Turbo Expo-2002, June 3–5, Amsterdam.
3.
Deol
,
D. L.
, 1993, “
Gas Path Analysis—Problems and Solutions
,” Symposium of Aircraft Integrated Monitoring Systems, Bonn, Germany, Sept. 21–23.
4.
Mathioudakis
,
K.
,
Kamboukos
,
P.
,
Stamatis
,
S.
, 2002, “
Turbofan Performance Deterioration Tracking Using Non-Linear Models and Optimization Techniques
,” ASME-TE-2002, June 3–5, Amsterdam, The Netherlands.
5.
Kamboukos
,
P.
, and
Mathioudakis
,
K.
, 2002, “
Comparison of Linear and Non-Linear Gas Turbine Performance Diagnostics
,” ASME-TE-2003, June 16–19, Atlanta, GA.
6.
Kobayashi
,
T.
, and
Simon
,
D. L.
, 2003, “
Application of a Bank of Kalman Filters for Aircraft Engine Fault Diagnostics
,” ASME-TE-2003, June 16–19, Atlanta, GA.
7.
Zedda
,
M.
, and
Singh
,
R.
, 1998, “
Fault Diagnosis of a Turbofan Engine Using Neural Networks: A Quantitative Approach
,” Paper No. AIAA 98-3062.
8.
Zedda
,
M.
, and
Singh
,
R.
, 1999, “
Gas Turbine Engine and Sensor Fault Diagnosis Using Optimisation Techniques
,” Paper No. AIAA 99–2530.
9.
Gulati
,
A.
, 2002, “
An Optimization Tool for Gas Turbine Engine Diagnostics
,” Ph.D. thesis, School of Engineering, Cranfield University, UK.
10.
Sampath
,
S.
,
Gulati
,
A.
, and
Singh
,
R.
, 2002, “
Fault Diagnostics Using Genetic Algorithm for Advanced Cycle Gas Turbine
,” ASME- TE-2002, June 3–5, Amsterdam, The Netherlands.
11.
Montgomery
,
D. C.
, 1984, “
Design and Analysis of Computer Experiments
,”
2nd ed.
,
Wiley
, New York.
12.
Myers
,
R. H.
, 1999, “
Response Surface Methodology Current Status and Future Direction
,”
J. Quality Technol.
0022-4065,
31
(
1
), pp.
30
74
.
13.
Bethke
,
A. D.
, 1981, “
Genetic Algorithm as Function Optimizers
,” Doctoral dissertation, University of Michigan.
14.
Bosworth
,
J.
,
Foo
,
N.
, and
Zeigler
,
B. P.
, 1972, “
Comparison of Genetic Algorithms With Conjugate Gradient Method
,” National Aeronautics and Space Administration, Washington, DC, Report No. CR-2093.
15.
Goldberg
,
D. E.
, 1989,
Genetic Algorithms in Search, Optimization and Machine Learning
,
Addison-Wesley
, Reading, MA.
16.
Ogaji
,
S. O. T.
, and
Singh
,
R.
, 2002, “
Gas Path Fault Diagnosis Framework for a 3-Shaft Gas Turbine
,”
IMechE Journal of Power and Energy
,
217
,
A3
.
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