Coupling principal component analysis and Kalman filtering algorithms for on-line aircraft engine diagnostics

Control Engineering Practice

Volumn 17, Issue 4, 2009, Pages 494-502

  Borguet, S ^a   Leonard O ^a  

^a UNIVERSITY OF LIÈGE   (Belgium)

Author keywords

Condition monitoring; Information fusion; Jet engine; Kalman filter; Principal component analysis

Indexed keywords

AIRCRAFT; AIRCRAFT ENGINES; CONDITION MONITORING; CONTROL THEORY; ENGINES; INFORMATION FUSION; JET ENGINES; KALMAN FILTERS; STRUCTURES (BUILT OBJECTS);

DIAGNOSTIC TOOLS; ENGINE CONDITIONS; ENGINE DIAGNOSTICS; ENGINE HEALTH MONITORING; INTENSIVE RESEARCHES; JET ENGINE; KALMAN FILTER; KALMAN FILTER TECHNIQUES; KALMAN FILTERING ALGORITHMS; MONITORING TOOLS; PRACTICAL IMPLEMENTATIONS;

PRINCIPAL COMPONENT ANALYSIS;

EID: 60249088035     PISSN: 09670661     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.conengprac.2008.09.008     Document Type: Article

References (29)

1. Amand, T., Heyen, G., & Kalitventzeff, B. (2000). Plant monitoring and fault detection: Synergy between data reconciliation and principal component analysis. Computers and Chemical Engineering, 24S.
2. Borguet, S., & Léonard, O. (2007). A sensor-fault-tolerant diagnosis tool based on a quadratic programming approach. In ASME Turbo Expo, number GT2007-27324.
3. Curnock, B. (2000). Obidicote project-word package 4: Steady-state test cases. Technical Report DNS62433, Rolls-Royce PLc.
4. de Boe, P., & Golinval, J. C. (2001). Damage localisation using principal component analysis of distributed sensor array. In Third workshop on structural health monitoring Stanford (USA).
5. Dewallef, P. (2005). Application of the Kalman filter to health monitoring of gas turbine engines: A sequential approach to robust diagnosis. Ph.D. thesis, University of Liège.
6. Dewallef, P., Mathioudakis, K., & Léonard, O. (2004). On-line aircraft engine diagnostic using a soft-constrained Kalman filter. In ASME Turbo Expo, number GT2004-53539.
7. Dewallef, P., Romessis, C., Mathioudakis, K., & Léonard, O. (2004). Combining classification techniques with Kalman filters for aircraft engine diagnostics. In ASME Turbo Expo, number GT2004-53541.
8. Doel D.L. Interpretation of weighted-least-squares gas path analysis results. ASME Journal of Engineering for Gas Turbines and Power 125 3 (2003) 624-633
9. Dunia R., and Qin S.J. Subspace identification to multidimensional fault identification and reconstruction. AIChE Journal 44 8 (1998) 1813-1831
10. Dunia R., Qin S.J., Edgar T.F., and McAvoy T.J. Identification of faulty sensors using principal component analysis. AIChE Journal 42 (1996) 2797-2812
11. Gomez, G., & Lendasse, A. (2000). Statistical fault isolation with principal component analysis. IFAC safe process 2000.
12. Haykin, S. (2001). Kalman filtering and neural networks. Wiley series on adaptive and learning systems for signal processing, communications and control. New York: Wiley.
13. Jackson J.E. A user's guide to principal component analysis (1991), Wiley-InterScience, New York
14. Jackson J.E., and Mudholkar G.S. Control procedures for residuals associated with principal component analysis. Technometrics 21 (1979) 341-349
15. Kalman R.E., and Bucy R.S. New results in linear filtering and prediction theory. Transactions on ASME, Series D, Journal of Basic Engineering 83 (1961)
16. Kobayashi, T., & Simon, D. L. (2001). A hybrid neural network-genetic algorithm technique for aircraft engine performance diagnostics. In 37th AIAA/ASME/SAE/ASEE joint propulsion conference, number AIAA-2001-3763.
17. Li Y.G. Performance-analysis-based gas turbine diagnostics: A review. IMechE Journal of Power and Energy 216 5 (2002) 363-377
18. Mathioudakis K., Kamboukos Ph., and Stamatis A. Turbofan performance deterioration tracking using non-linear models and optimization techniques. ASME Journal of Engineering for Gas Turbines and Power 124 (2002) 580-587
19. Provost, M. J. (2003). Kalman filtering applied to gas turbine analysis. In Von Karman Institute lecture series, number 01 in Gas turbine condition monitoring and fault diagnosis.
20. Rajamani, R., Wang, J., & Jeong, K. Y. (2004). Condition-based maintenance for aircraft engines. In ASME Turbo Expo, number GT2004-54127.
21. Romessis, C., Kyriazis, A., & Mathioudakis, K. (2007). Fusion of gas turbines diagnostic inference-the Dempster-Schafer approach. In ASME Turbo Expo, number GT2007-27043.
22. Romessis, C., & Mathioudakis, K. (2002). Setting up of a probabilistic neural network for sensor fault detection including operation with component faults. In ASME Turbo Expo, number GT2002-30035.
23. Shlens, J. (2005). A tutorial on principal component analysis. Technical Report, University of California, San Diego. URL: 〈http://www.snl.salk.edu/∼shlens/pub/notes/pca.pdf〉.
24. Stamatis, A., Mathioudakis, K., Ruiz, J., & Curnock, B. (2001). Real-time engine model implementation for adaptive control and performance monitoring of large civil turbofans. In ASME Turbo Expo, number 2001-GT-0362.
25. Urban, L. A. (1972). Gas path analysis applied to engine condition monitoring. In 8th joint propulsion specialist conference, number AIAA-72-1082.
26. Volponi A., DePold H., Ganguli R., and Daguang C. The use of Kalman filter and neural network methodologies in gas turbine performance diagnostic: A comparative study. ASME Journal of Engineering for Gas Turbines and Power 125 (2003) 917-924
27. Volponi, A. J. (2003). Foundation of gas path analysis (part i and ii). In von Karman Institute lecture series, number 01 in Gas turbine condition monitoring and fault diagnosis.
28. Volponi, A. J. (2005). Data fusion for enhanced aircraft engine prognostics and health management. Contractor Report CR-2005-214055, NASA.
29. Wallin, M., & Grönstedt, T. (2004). A comparative study of genetic algorithms and gradient methods for rm12 turbofan engine diagnostics and performance estimation. In ASME Turbo Expo, number GT2004-53591.