Stochastic Hybrid System Actuator Fault Diagnosis by Adaptive Estimation

Qinghua Zhang (1, 2)

1 IFSTTAR/COSYS - Département Composants et Systèmes
2 I4S - Statistical Inference for Structural Health Monitoring

Based on the interacting multiple model (IMM) estimator for hybrid system state estimation and on the adaptive Kalman filter for time varying system joint state-parameter estimation, a new algorithm, the adaptive IMM estimator, is proposed in this paper for actuator fault diagnosis in stochastic hybrid systems. The working modes of the considered hybrid systems are described by stochastic state-space models, and the mode transitions are characterized by a Markov model. Actuator faults are modeled as parameter changes, and the related fault diagnosis problem is solved by the proposed adaptive IMM estimator through joint state-parameter estimation. Numerical examples are presented to illustrate the performance of the proposed method.

Mots clés: Fault diagnosis; hybrid system; random uncertainty; actuator fault; joint state-parameter estimation

Domaines: Automatique

Qinghua Zhang. Stochastic Hybrid System Actuator Fault Diagnosis by Adaptive Estimation. 9th IFAC Symposium on Fault Detection, Supervision and Safety of Technical Processes (SAFEPROCESS), Sep 2015, Paris, France. ⟨hal-01232155⟩