A fuzzy logic system is developed for gas turbine module fault isolation. Inputs to the fuzzy logic system are measurement deviations of gas path parameters from a “good” baseline engine. The gas path measurements used are exhaust gas temperature, low and high rotor speed, and fuel flow. These sensor measurements are available on most jet engines. The fuzzy logic system uses rules developed from performance influence coefficients to isolate the module fault while accounting for uncertainty in the gas path measurements. Tests with simulated data show the fuzzy system isolates module faults with accuracy of over 95%. In addition, the fuzzy logic system shows good performance even with poor quality data. Additional pressure and temperature measurements between the compressor and before the burner help to increase the accuracy of fault isolation at high levels of uncertainty and when modeling assumptions weaken.

Issue Section:
Gas Turbines: Controls, Diagnostics, and Instrumentation
Keywords:
fuzzy logic, aerospace engines, modelling, compressors, gas turbines
Topics:
Engines, Fuzzy logic, Uncertainty, Sensors, Gas turbines, Fingerprints, Compressors, Jet engines
1.
Urban, L. A., 1972, “Gas Path Analysis Applied to Turbine Engine Conditioning Monitoring,” AIAA/SAE Paper 72-1082.
2.
Volponi, A., 1983, “Gas Path Analysis: An Approach to Engine Diagnostics,” Time-Dependent Failure Mechanisms and Assessment Methodologies, Cambridge University Press, Cambridge, UK.
3.
Volponi, A. J., and Urban, L. A., 1992, “Mathematical Methods of Relative Engine Performance Diagnostics,” SAE Trans., 101; Journal of Aerospace Technical Paper 922048.
4.
Doel, D. L., 1994, “TEMPER—A Gas Path Analysis Tool for Commercial Jet Engines,” ASME Paper 92-GT-315.
5.
Stamatis, A., Mathioudakis, K., Berios, K., and Papailiou, K. D., 1991, “Jet Engine Fault Detection with Discrete Operating Points Gas Path Analysis,” J. Propulsion, 7(6), pp. 1043–1048.
6.
Merrington, G. L., 1993, “Fault Diagnosis in Gas Turbines Using a Model Based Technique,” ASME Paper 93-GT-13.
7.
Luppold, R. H., Roman, J. R., Gallops, G. W., and Kerr, L. J., 1989, “Estimating In-Flight Engine Performance Variations Using Kalman Filter Concepts,” AIAA Paper 89-2584.
8.
Gallops, G. W., and Bushman, M. A., 1992, “In-Flight Performance Diagnostic Capability of an Adaptive Engine Model,” AIAA Paper 92-3746.
9.
Kerr, L. J., Nemec, T. S., and Gallops, G. W., 1991, “Real-Time Estimation of Gas Turbine Engine Damage Using a Control Based Kalman Filter Algorithm,” ASME Paper 91-GT-216.
10.
De Pold
,
H.
, and
Gass
,
1999
, “
The Application of Expert Systems and Neural Networks to Gas Turbine Prognostics and Diagnostics
,”
ASME J. Eng. Gas Turbines Power
,
121
, pp.
607
–
612
.
11.
Lu
,
P. J.
,
Hsu
,
T. C.
,
Zhang
,
M. C.
,
Zhang
,
J.
,
2000
, “
An Evaluation of Engine Fault Diagnostics Using Artificial Neural Networks
,”
ASME J. Eng. Gas Turbines Power
,
123
,
240
–
246
.
12.
Volponi, A. J., DePold, H., Ganguli, R., and Daguang, C., 2000, “The Use of Kalman Filter and Neural Network Methodologies in Gas Turbine Performance Diagnostics: A Comparative Study,” ASME Paper 00-GT-547.
13.
Adlassing
,
K. D.
,
1986
, “
Fuzzy Set Theory in Medical Diagnostics
,”
IEEE Trans. Syst. Man Cybern.
,
16
, pp.
260
–
264
.
14.
Tazaki, E., et al., 1986, “Development of Automated Health Testing and Services System via Fuzzy Reasoning,” Proc. IEEE Int. Conf. On Systems, Man and Cybernetics.
15.
Hong
,
X. L.
, and
Chen
,
P. C. L.
,
2000
, “
The Equivalence between Fuzzy Logic Systems and Feedforward Neural Networks
,”
IEEE Trans. Neural Netw.
,
11
(
2
), pp.
103
–
111
.
16.
Zadeh
,
L.
,
1996
, “
Fuzzy Logic=Computing With Words
,”
IEEE Trans. Fuzzy Syst.
,
4
(
2
), pp.
103
–
111
.
17.
Kosko, B., 1997, Fuzzy Engineering, Prentice-Hall, Englewood Cliffs, NJ.
18.
Chi, Z., Yan, H. M., and Pham, T., 1998, Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition, World Scientific, Singapore.
19.
Chi
,
Z.
, and
Yan
,
H.
,
1993
, “
ID3 Derived Fuzzy Rules and Optimized Defuzzification for Handwritten Character Recognition
,”
IEEE Trans. Fuzzy Syst.
,
4
(
1
), pp.
24
–
31
.
20.
Wang
,
L. X.
, and
Mendel
,
J. M.
,
1992
, “
Generating Fuzzy Rules by Learning From Examples
,”
IEEE Trans. Syst. Man Cybern.
,
22
(
6
), pp.
1414
–
1427
.
21.
Abe
,
S.
, and
Lan
,
M. S.
,
1995
, “
A Method for Fuzzy Rules Extraction Directly From Numerical Data and Its Application to Pattern Recognition
,”
IEEE Trans. Fuzy Syst.
,
3
, pp.
18
–
28
.
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