An Intelligent Optimization-Based Particle Filter for Fault Diagnosis

It is very important to implement the fault diagnosis technology in industrial processes to make the process more reliable. In this paper, an improved particle filter (PF) method based on a modified beetle swarm antennae search (BSAS) algorithm is proposed and verified in a doubly fed induction generator (DFIG) fault diagnosis application. Firstly, the search strategy of BSAS is improved to ensure its global search ability. Secondly, it is introduced to the traditional PF algorithm to improve the particle diversity and impoverishment drawbacks. Finally, the fault diagnosis algorithm is verified by combining the DFIG state space model. The simulation experimental of fault detection and isolation results show that the proposed method is simple and effective, and it can effectively monitor the occurrence of faults. For the fault diagnostic application, the method proposed in this paper could be implemented in other model based processes, including chemical process, biochemical wastewater treatment process, etc.