0
TECHNICAL PAPERS: Gas Turbines: Controls, Diagnostics, and Instrumentation

Application of Fuzzy Logic for Fault Isolation of Jet Engines

[+] Author and Article Information
R. Ganguli

Department of Aerospace Engineering, Indian Institute of Science, Bangalore 560 012, India

J. Eng. Gas Turbines Power 125(3), 617-623 (Aug 15, 2003) (7 pages) doi:10.1115/1.1470481 History: Received December 01, 2000; Revised March 01, 2001; Online August 15, 2003
Copyright © 2003 by ASME
Your Session has timed out. Please sign back in to continue.

References

Urban, L. A., 1972, “Gas Path Analysis Applied to Turbine Engine Conditioning Monitoring,” AIAA/SAE Paper 72-1082.
Volponi, A., 1983, “Gas Path Analysis: An Approach to Engine Diagnostics,” Time-Dependent Failure Mechanisms and Assessment Methodologies, Cambridge University Press, Cambridge, UK.
Volponi, A. J., and Urban, L. A., 1992, “Mathematical Methods of Relative Engine Performance Diagnostics,” SAE Trans., 101 ; Journal of Aerospace Technical Paper 922048.
Doel, D. L., 1994, “TEMPER—A Gas Path Analysis Tool for Commercial Jet Engines,” ASME Paper 92-GT-315.
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.
Merrington, G. L., 1993, “Fault Diagnosis in Gas Turbines Using a Model Based Technique,” ASME Paper 93-GT-13.
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.
Gallops, G. W., and Bushman, M. A., 1992, “In-Flight Performance Diagnostic Capability of an Adaptive Engine Model,” AIAA Paper 92-3746.
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.
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.
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, pp. 240–246.
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.
Adlassing,  K. D., 1986, “Fuzzy Set Theory in Medical Diagnostics,” IEEE Trans. Syst. Man Cybern., 16, pp. 260–264.
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.
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.
Zadeh,  L., 1996, “Fuzzy Logic=Computing With Words,” IEEE Trans. Fuzzy Syst., 4(2), pp. 103–111.
Kosko, B., 1997, Fuzzy Engineering, Prentice-Hall, Englewood Cliffs, NJ.
Chi, Z., Yan, H. M., and Pham, T., 1998, Fuzzy Algorithms: With Applications to Image Processing and Pattern Recognition, World Scientific, Singapore.
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.
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.
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.

Figures

Grahic Jump Location
Impact of coupling factor on low-pressure compressor (LPC) fault isolation success rate with different sensor suites
Grahic Jump Location
Impact of coupling factor on high-pressure compressor (HPC) fault isolation success rate with different sensor suites
Grahic Jump Location
Impact of coupling factor on fan (FAN) fault isolation success rate with different sensor suites
Grahic Jump Location
Success rate in low-pressure compressor (LPC) fault isolation with additional sensors and increasing levels of uncertainty
Grahic Jump Location
Success rate in high-pressure compressor (HPC) fault isolation with additional sensors and increasing levels of uncertainty
Grahic Jump Location
Success rate in fault isolation for uncertainty levels different from design point (= 0)
Grahic Jump Location
Gaussian functions defining linguistic measures for fuzzy set for low-spool rotor speeds (N1) delta
Grahic Jump Location
Gaussian functions defining linguistic measures for fuzzy sets for high-spool rotor speeds (N2) delta
Grahic Jump Location
Gaussian functions defining linguistic measures for fuzzy sets for fuel flow (WF) delta
Grahic Jump Location
Gaussian functions defining linguistic measures for fuzzy set for exhaust gas temperature (EGT) delta
Grahic Jump Location
Fingerprint chart showing change in low-spool rotor speed (N1) for a module efficiency change of −2%
Grahic Jump Location
Fingerprint chart showing change in high-spool rotor speed (N2) for a module efficiency change of −2%
Grahic Jump Location
Fingerprint chart showing change in exhaust gas temperature (EGT) for a module efficiency decrease of −2%
Grahic Jump Location
Fingerprint chart showing change in fuel flow (WF) for a module efficiency decrease of −2%

Tables

Errata

Discussions

Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In