Fault Diagnosis and RUL Prediction of Nonlinear Mechatronic System via Adaptive Genetic Algorithm-Particle Filter

This paper proposes a real-time model-based health monitoring method for a nonlinear mechatronic system with multiple faults in both parametric and nonparametric components. A nonlinear bond graph model incorporating the influence of Stribeck friction is established to capture the dynamic behavior of the monitored mechatronic system. Based on the model, fault diagnosis is carried out via the combinative fault signature matrix which is built from independent and dependent analytical redundancy relations to enhance the isolation ability of the monitored system under multiple-fault condition. After that, an adaptive genetic algorithm-particle filter (AGA-PF) is developed for fault parameter estimation and remaining useful life prediction. The AGA-PF can mitigate the sample impoverishment problem in generic particle filter through genetic operators with adaptive mutation probability according to the fitness of the particles. The effectiveness of the proposed method is verified through simulation and experiment investigations on the nonlinear mechatronic system.