Health Monitoring and Prognosis of Hybrid Systems
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Abstract
Maintenance and repair of complex systems are an increasing part of the total cost of final product. Efficient diagnosis and prognosis techniques have to be adopted to detect, isolate and anticipate faults. Moreover the recent industrial systems are naturally hybrid: their dynamic behavior is both continuous and discrete. This paper presents an architecture of health monitoring and prognosis for hybrid systems. By using model and experience-based approach we propose an implementation of an integrated diagnosis/prognosis process based on Weibull probabilistic model. This article focuses, particularly on the prognosis algorithm description. The pro-cess has been implemented and tested on Matlab. Simulation results on a water tank system show how prognosis and diagnosis interact into the architecture.
How to Cite
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health monitoring, Hybrid Systems, Fault Diagnosis and Prognosis
Bayoudh, M., Trave ́-Massuye`s, L., & Olive, X. (2008). Hy- brid systems diagnosis by coupling continuous and discrete event techniques. In Proceedings of the 17th IFAC World Congress (pp. 7265–7270). Korea, South. doi: 10.3182/20080706-5-KR-1001.01229
Cao, X. (1989). The predictability of discrete event systems. IEEE Transactions Automatic Control, 34(11), 1168– 1171. doi: 10.1109/9.40745
Castaneda, G.-A. P., Aubry, J.-F., & Brinzei, N. (2010). DyReIA (Dynamic Reliability and Assessment). In Proceedings of the 1st Workshop on DYnamic Aspects in DEpendability Models for Fault-Tolerant Systems (DYADEM-FTS). Valencia, Spain. doi: 10.1145/1772630.1772642
Chanthery, E., & Ribot, P. (2013). An integrated framework for diagnosis and prognosis of hybrid systems. In Proceedings of the 3rd Workshop on Hybrid Autonomous
System (HAS). Roma, Italy.
Ferreiro, S., & Arnaiz, A. (2008). Prognosis based on probabilistic models and reliability analysis to improve aircraft maintenance. In Proceedings of the International Conference on Prognostics and Health Management (PHM). Denver, USA.
Genc, S., & Lafortune, S. (2009). Predictability of event occurrences in partially-observed discrete- event systems. Automatica, 45(2), 301–311. doi: 10.1016/j.automatica.2008.06.022
Hall, P. L., & Strutt, J. E. (2003). Probabilistic physics-of- failure models for component reliabilities using monte carlo simulation and weibull analysis: a parametric study. Reliability Engineering and System Safety, 80, 233–242. doi: 10.1016/S0951-8320(03)00032-2
Henzinger, T. (1996). The theory of hybrid automata. In Proceedings of the 11th Annual IEEE Symposium on Logic in Computer Science (pp. 278–292). doi: 10.1109/LICS.1996.561342
Je ́ron, T., Marchand, H., Genc, S., & Lafortune, S. (2008). Predictability of sequence patterns in discrete event systems. In Proceedings of the 17th IFAC World Congress (pp. 537–543). Korea, South. doi: 10.3182/20080706-5-KR-1001.00091
Khoumsi, A. (2009). Fault prognosis in real-time discrete event systems. In Proceedings of the International Workshop on Principles of Diagnosis (DX) (p. 259). Stockholm, Sweden.
Rausand, M., & Hoyland, A. (2004). System reliability theory: models, statistical methods and applications. Wiley. doi: 10.1002/9780470316900
Ribot, P., & Bensana, E. (2011). A generic adaptative prognostic function for heterogeneous multi-component systems: application to helicopters. In Proceedings of the European Safety & Reliability Conference (ES- REL). Troyes, France. doi: 10.1201/b11433-53
Ribot, P., Pencole ́, Y., & Combacau, M. (2009). Diagnosis and prognosis for the maintenance of complex systems. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (SMC). San Anto- nio, USA. doi: 10.1109/ICSMC.2009.5346718
Roychoudhury, I., & Daigle, M. (2011, October 4-7). An integrated model-based diagnostic and prognostic framework. In Proceedings of the 22nd International Workshop on Principle of Diagnosis (DX’11). Murnau, Ger- many.
Sampath, M., Sengputa, R., Lafortune, S., Sinnamohideen, K., & Teneketsis, D. (1995). Diagnosability of discrete-event systems. IEEE Transactions on Automatic Control, 40, 1555-1575. doi: 10.1109/9.412626
Vachtsevanos, G., Lewis, L., Roemer, M., Hess, A., & Wu, B. (2006). Intelligent fault diagnosis and prognosis for engineering systems. Wiley. doi: 10.1002/9780470117842.index
Zemouri, R., & Faure, J.-M. (2006, October 4-6). Diagnosis of discrete event system by stochastic timed automata. In the IEEE International Conference on Control Applications (pp. 1861–1866). Munich, Germany. doi: 10.1109/CACSD-CCA-ISIC.2006.4776924
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