Fault detection and isolation in aircraft gas turbine engines. Part 2: Validation on a simulation test bed

Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering

Volumn 222, Issue 3, 2008, Pages 319-330

  Sarkar, S ^a   Yasar, M ^a,b   Gupta, S ^a   Ray, A ^a   Mukherjee, K ^a  

^a The Pennsylvania State University   (United States)

^b TECHNO SCIENCES INC   (United States)

Author keywords

Aircraft gas turbine engines; Fault detection and isolation; Symbolic dynamic filtering

Indexed keywords

ADMINISTRATIVE DATA PROCESSING; AIRCRAFT; AIRCRAFT ENGINES; ALGORITHMS; COMPUTER NETWORKS; CONTROL SYSTEMS; DYNAMICAL SYSTEMS; DYNAMICS; ELECTRIC GENERATORS; ENGINES; EQUIPMENT TESTING; EVOLUTIONARY ALGORITHMS; FEATURE EXTRACTION; FILTRATION; FINANCIAL DATA PROCESSING; FLIGHT CONTROL SYSTEMS; GAS ENGINES; GAS TURBINE POWER PLANTS; GAS TURBINES; GASES; INFORMATION SCIENCE; MATHEMATICAL MODELS; MECHANICS; PAPER; PATTERN RECOGNITION; TESTING; TIME SERIES ANALYSIS; TURBINES; TURBOMACHINERY;

AIRCRAFT GAS TURBINE ENGINES; DYNAMIC FILTERING; ENGINE COMPONENTS; ENGINE CONTROL SYSTEMS; FAULT DETECTION AND ISOLATION (FDI); GAS PATH; GAS-TURBINE ENGINES; INTEGRATED MODELLING; NOVEL CONCEPT; REAL-TIME DETECTION; SIMULATION TESTED; STATISTICAL PATTERN RECOGNITION; TEST BEDS; TIME SERIES DATA;

FAULT DETECTION;

EID: 45749107071     PISSN: 09544100     EISSN: None     Source Type: Journal    
DOI: 10.1243/09544100JAERO312     Document Type: Article

References (21)

1. Razak, A. M. Y. and Carlyle, J. S. An advanced model based health monitoring system to reduce gas turbine ownership cost. ASME Turbo Expo 2000, Munich, Germany, 2000, paper no. ASME-2000-GT-627.
2. Volponi, A. J., DePold, H., Ganguli, R., and Chen, D. The use of Kalman filter and neural network methodologies in gas turbine performance diagnostics: a comparative study. ASME Turbo Expo 2000, Munich, Germany, 2000, paper no. ASME-2000-GT-547.
3. Moller, J. C, Litt, J. S., and Guo, T. H. Neural network-based sensor validation for turboshaft engines. In 34th Joint Propulsion Conference cosponsored by AIAA, ASME, SAE, and ASEE, Cleveland, OH, USA, 1998, paper no. AIAA-98-3547.
4. Gupta, S., Ray, A., and Keller, E. Symbolic time series analysis of ultrasonic data for early detection of fatigue damage. Mech. Syst. Signal Process., 2007,21 (2), 866-884.
5. Gupta, S. and Ray, A. Real-time fatigue life estimation in mechanical systems. Meas. Sci. Technol., 2007, 18(7), 1947-1957.
6. Badii, R. and Politi, A. Complexity, hierarchical structures and scaling in physics, 1997 (Cambridge University Press, Cambridge, UK).
7. Simani, S., Fantuzzi, C., and Patton, R. Model-based fault diagnosis in dynamical systems using identification techniques, 2003 (Springer-Verlag, London, UK).
8. Gupta, S., Ray, A., Sarkar, S. and Yasar, M. Fault detection and isolation in aircraft gas turbine engines. Part 1. Underlying concept. Proc. IMechE, Part G: J. Aerospace Engineering, 2008, 222(G3), 307-318 (this issue).
9. Ray, A. Symbolic dynamic analysis of complex systems for anomaly detection. Signal Process., 2004, 84(7), 1115-1130.
10. Rajagopalan, V. and Ray, A. Symbolic time series analysis via wavelet-based partitioning. Signal Process., 2006, 86(11), 3309-3320.
11. Diao, Y. and Passino, K. M. Stable fault-tolerant adaptive fuzzy/neural control of a turbofan engine. IEEE Trans. Control Syst. Technol., 2001, 9(3), 494-509.
12. Parker, K. I. and Guo, T. H. Development of a turbo-fan engine simulation in a graphical simulation environment. In JANNAF Aero-Propulsion Subcommittee Meeting, Destin, FL, USA, 2002.
13. Adibhatla, S. and Johnson, K. L. Evaluation of nonlinear PSC algorithm on a variable cycle engine. In Proceedings of the AIAA 29th Joint Propulsion Conference and Exhibit, Monterey, CA, USA, 1993.
14. Kobayashi, T. and Simon, D. L. A hybrid neural network-genetic algorithm technique for aircraft engine performance diagnostics. In 37th Joint Propulsion Conference and Exhibit cosponsored by the AIAA, ASME, SAE, and ASEE, Salt Lake City, Utah, 2001, paper no. AIAA 2001-3763.
15. Tanenbaum, A. S. Computer networks, 4th edition, 2003 (Prentice Hall PTR, Upper Saddle River, NJ, USA).
16. Rajagopalan, V., Chakraborty, S. and Ray, A. Estimation of slowly-varying parameters in nonlinear systems via symbolic dynamic filtering. Signal Process., 2008, 88(2), 339-348.
17. Mallat, S. A wavelet tour of signal processing, 2nd edition, 1998 (Academic Press, Boston, MA, USA).
18. Mushini, R. and Simon, D. On optimization of sensor selection for aircraft gas turbine engines. In Proceedings of the 18th International Conference on Systems Engineering (ICSENG '05), Las Vegas, ND, USA, 2005.
19. Duda, R. O., Hart, P. E., and Stork, D. G. Pattern classification, 2001 (John Wiley, New York, USA).
20. Bishop, C. M. Neural networks for pattern recognition, 1995 (Oxford University Press, New York, NY, USA).
21. Hagan, M. T., Demuth, H. B., and Beale, M. H. Neural network design, 1996 (PWS Publishing, Boston, MA, USA).