HPPN-based Prognosis for Hybrid Systems



Published Oct 2, 2017
Pauline Ribot Elodie Chanthery Quentin Gaudel


This paper presents a model-based prognosis method for hybrid systems i.e. that have both discrete and continuous behaviors. The current state of the hybrid system is estimated by a diagnosis process and the prognosis process uses this state estimation to predict the future states and to determine the end of life (EOL) or the remaining useful life (RUL) of the system. The Hybrid Particle Petri Nets (HPPN) formalism is used to model the hybrid system behavior and degradation. A HPPN-based diagnoser has already been defined to provide a current state estimation that takes uncertainty about the system model and observations into account. We propose to generate a prognoser from the HPPN model of the system.
This prognoser is initialized and updated with the result of the HPPN-based diagnoser. It computes a distribution of beliefs over the future mode trajectories of the system and predicts the system RUL/EOL. The prognosis methodology is demonstrated on a three tanks example.

How to Cite

Ribot, P., Chanthery, E., & Gaudel, Q. (2017). HPPN-based Prognosis for Hybrid Systems. Annual Conference of the PHM Society, 9(1). https://doi.org/10.36001/phmconf.2017.v9i1.2480
Abstract 213 | PDF Downloads 101



Hybrid Systems, model-based prognosis

Basile, F., Chiacchio, P., & Tommasi, G. D. (2009). Fault diagnosis and prognosis in Petri Nets by using a single generalized marking estimation. In 7th IFAC SAFEPROCESS.
Bayoudh, M., Travé-Massuyes, L., & Olive, X. (2008). Hybrid systems diagnosis by coupling continuous and discrete event techniques. In IFAC World Congress (pp. 7265–7270).
Chanthery, E., & Ribot, P. (2013). An Integrated Framework for Diagnosis and Prognosis of Hybrid Systems. In 3rd Workshop on Hybrid Autonomous System. Italy.
Daigle, M., Roychoudhury, I., & Bregon, A. (2015). Modelbased Prognostics of Hybrid Systems. In Annual Conf. of the PHM Society. USA.
Engel, S., Gilmartin, B., Bongort, K., & Hess, A. (2000). Prognostics, the real issues involved with predicting life remaining. In Aerospace Conf., IEEE (Vol. 6, p. 457-469).
Gaudel, Q., Chanthery, E., & Ribot, P. (2014). Health Monitoring of Hybrid Systems Using Hybrid Particle Petri Nets. In Annual Conf. of the PHM Society. USA.
Gaudel, Q., Chanthery, E., & Ribot, P. (2015). Hybrid Particle Petri Nets for Systems Health Monitoring under Uncertainty. Int. Journal of PHM, 6(022).
Lefebvre, D. (2014). Fault Diagnosis and Prognosis With Partially Observed Petri Nets. IEEE Trans. on Systems, Man, and Cybernetics, 44(10), 1413–1424.
Ribot, P., Pencolé, Y., & Combacau, M. (2013). Generic characterization of diagnosis and prognosis for complex heterogeneous systems. Int. Journal of Prognostics and Health Management, 4(023).
Vianna, W. O. L., & Yoneyama, T. (2015). Interactive Multiple-Model Application for Hydraulic Servovalve Health Monitoring. In Annual Conf. of the PHM Society. USA.
Yu, M., Wang, D., & Luo, M. (2014). Model-Based Prognosis for Hybrid Systems With Mode-Dependent Degradation Behaviors. IEEE Trans. on Industrial Electronics, 61(1), 546–554.
Zabi, S., Ribot, P., & Chanthery, E. (2013). Health Monitoring and Prognosis of Hybrid Systems. In Annual Conf. of the PHM Society.
Technical Research Papers