Fault Diagnosis with Dynamic Fuzzy Discrete Event System Approach
Determining faults is a challenging task in complex systems. A discrete event system (DES) or a fuzzy discrete event system (FDES) approach with a fuzzy rule-base may resolve the ambiguity in a fault diagnosis problem especially in the case of multiple faults. In this study, an FDES approach with a fuzzy rule-base is used as a means of indicating the degree and priority of faults, especially in the case of multiple faults The fuzzy rule-base is constructed using event-fault relations. Fuzzy events occurring any time with different membership degrees are obtained using k-means clustering algorithm. The fuzzy sub-event sequences are used to construct super events. The study is concluded by giving some examples about the distinguishability of fault types (parameter, actuator) in an unmanned small helicopter.
KeywordsFault Diagnosis Membership Degree Multiple Fault Discrete Event System Actuator Fault
Unable to display preview. Download preview PDF.
- 4.Karasu, Ç.: Small-sized unmanned model helicopter guidance and control. M.Sc. thesis, Middle East Technical University Ankara Turkey (November 2004)Google Scholar
- 5.Vinay, B., et al.: Diagnosis of helicopter gearboxes using structure-based networks. In: Proc.of the American Control Conference, Seattle Washington, June, pp. 1623–1627 (1995)Google Scholar
- 6.Constantino, R., et al.: Failure detection and identification and fault tolerant control using the IMM-KF with applications to the Eagle-Eye UAV. In: Proceedings of the 37th IEEE Conference on Decision & Control, Tampa Florida USA, December 1998, pp. 4208–4213 (1998)Google Scholar
- 7.Sampath, M., et al.: Failure diagnosis using discrete event models. IEEE Trans. on CST 4(2), 105–124 (1996)Google Scholar
- 13.Wang, W.: Identification of gear mesh signals by Kurtosis maximisation and its application to CH46 helicopter gearbox data. In: Proceeding of the 11th IEEE signal processing workshop on statistical signal processing, August 6-8, 2001, pp. 369–372 (2001)Google Scholar
- 14.Yu, X.H.: Actuator fault compensation for a helicopter model. In: Proceedings of IEEE Conference on Control Applications, vol. 1, pp. 1372–1374 (2003)Google Scholar
- 15.Amulya, K., et al.: Hybrid reasoning for prognostic learning in CBM sytems. In: IEEE Aerospace Conference Proceedings, March 10-17, vol. 6, pp. 2957–2969 (2001)Google Scholar
- 18.Fuessel, D., Isermann, R.: Hierarchical motor diagnosis utilizing structural knowledge and self-learning neuro-fuzzy scheme. IEEE Trans. on Industrial Electronics 47(5) (October 2000)Google Scholar
- 20.Wen, F., Deb, S.: Signal processing and fault detection with application to CH-46 helicopter data. In: IEEE Aerospace Conference Proceedings, March 18-25, 2000, vol. 6, pp. 15–26 (2000)Google Scholar
- 23.Ulieru, M.: Fuzzy logic in diagnostic decision: Probabilistic networks. Ph.D. dissertation, Technical University of Darmstadt, Germany (1996)Google Scholar
- 25.Ying, H., et al.: A fuzzy discrete event system for HIV/AIDS treatment planning. In: IEEE International Conf. on Fuzzy Systems, Budapest, Hungary, vol. 25-29, pp. 197–202 (July 2004)Google Scholar