Hybrid state estimation and fault diagnosis algorithm of hybrid systems using particle filter

Hybrid systems are composed of discrete event dynamic systems and continuous time dynamic systems, which interact with each other. It leads to that the fault diagnosis of hybrid systems is particularly difficult. In order to expand the scope of application and improve the diagnosis efficiency, a hybrid state estimation based hybrid systems fault diagnosis method is proposed. Considering the controlled migration, the autonomous migration and the stochastic migration of hybrid systems, the discrete states (including fault states) and continuous states of the system are modeled based on the stochastic hybrid automaton. The common extended Kalman particle filter based hybrid estimation algorithm is developed so as to be applied in the hybrid estimation of discrete and continuous states produced by the migrations of hybrid systems. Finally, the fault diagnosis can be achieved rapidly according to the estimated result of discrete states. A simulation experiment is employed to conduct the fault diagnosis on a typical nonlinear hybrid system, and the results indicate that this method is effective.