Health Monitoring of Hybrid Systems Using Hybrid Particle Petri Nets

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Quentin Gaudel Elodie Chanthery Pauline Ribot

Abstract

This paper presents an approach of model-based diagnosis for the health monitoring of hybrid systems. These systems have both continuous and discrete dynamics. Modified Particle Petri Nets, initially defined in the context of hybrid systems mission monitoring, are extended to estimate the health state of hybrid systems. This formalism takes into account both uncertainties about the system knowledge and about diagnosis results. The generation of a diagnoser is proposed to track online the system health state under uncertainties by using particle filter. To include more complex characteristics of the system, as its degradations for prognosis purpose, an enriched formalism called Hybrid Particle Petri Nets is defined.

How to Cite

Gaudel, Q. ., Chanthery, E. ., & Ribot, P. (2014). Health Monitoring of Hybrid Systems Using Hybrid Particle Petri Nets. Annual Conference of the PHM Society, 6(1). https://doi.org/10.36001/phmconf.2014.v6i1.2354
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Keywords

health monitoring, Hybrid Systems, Model-based diagnosis, particle Petri nets

References
Bayoudh, M., Trave ́-Massuyes, L., & Olive, X. (2008). Hybrid systems diagnosis by coupling continuous and discrete event techniques. In Proceedings of the IFAC World Congress (pp. 7265–7270). Seoul, Korea.

Chanthery, E., & Ribot, P. (2013). An integrated framework for diagnosis and prognosis of hybrid systems. In 3rd Workshop on Hybrid Autonomous System (HAS). Roma, Italy.

Cocquempot, V., El Mezyani, T., & Staroswiecki, M. (2004). Fault detection and isolation for hybrid systems using structured parity residuals. In 5th Asian Control Conference. (Vol. 2, pp. 1204–1212).

Gaudel, Q., Chanthery, E., Ribot, P., & Le Corronc, E. (2014). Hybrid systems diagnosis using modified particle petri nets. In Proceedings of the 25th International Work- shop on
Principles of Diagnosis (DX’14). Graz, Aus- tria.

Henzinger, T. (1996). The theory of hybrid automata. In Proceedings of the 11th Annual IEEE Symposium on Logic in Computer Science (pp. 278–292).

Lesire, C., & Tessier, C. (2005). Particle petri nets for aircraft procedure monitoring under uncertainty. In Applications and Theory of Petri Nets (pp. 329–348). Springer.

Ribot, P., Pencole ́, Y., & Combacau, M. (2013). Generic characterization of diagnosis and prognosis for complex heterogeneous systems. International Journal of Prognostics and Health Management, 4.

Roychoudhury, I., & Daigle, M. (2011). An integrated model- based diagnostic and prognostic framework. In Proceedings of the 22nd International Workshop on Principle of Diagnosis (DX’11). Murnau, Germany.

Sampath, M., Sengupta, R., Lafortune, S., Sinnamohideen, K., & Teneketzis, D. (1995). Diagnosability of discrete-event systems. IEEE Transactions on Automatic Control, 40(9), 1555–1575.

Staroswiecki, M., & Comtet-Varga, G. (2001). Analytical redundancy relations for fault detection and isolation in algebraic dynamic systems. Automatica, 37(5), 687– 699.

Vinson, G., Ribot, P., Prado, T., & Combacau, M. (2013). A generic diagnosis and prognosis framework: application to permanent magnets synchronous machines. In IEEE Prognostics and System Health Management Conference (PHM) (pp. 1039–1044). Milano, Italy.

Zouaghi, L., Alexopoulos, A., Wagner, A., & Badreddin, E. (2011a). Modified particle petri nets for hybrid dynamical systems monitoring under environmental un- certainties. In IEEE/SICE International Symposium on System Integration (SII) (pp. 497–502).

Zouaghi, L., Alexopoulos, A., Wagner, A., & Badreddin, E. (2011b). Probabilistic online-generated monitoring models for mobile robot navigation using modified petri net. In 15th International Conference on Ad-
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Technical Papers