A fault diagnosis method of rolling element bearing based on improved PSO and BP neural network

Article type: Research Article

Authors: Song, Xudong | Wang, Hao^{; *} | Liu, Yifan | Wang, Zi | Cui, Yunxian

Affiliations: Big data & Intelligent System Lab, Dalian Jiaotong University, China

Correspondence: [*] Corresponding author. Big data & Intelligent system Lab, Dalian Jiaotong University, Liaoning, China. E-mail: [email protected].

Abstract: Aiming at the inherent defects of BP neural network in the field of rolling bearing fault diagnosis, based on the optimization of particle swarm optimization algorithm, this paper uses a variety of optimization strategies to optimize the particle swarm optimization algorithm, and then uses the optimized particle swarm optimization algorithm to optimize the BP neural network. Therefore, a new fault diagnosis method (Dual Strategy Particle Swarm Optimization BP neural network, DSPSOBP) is proposed. DSPSOBP fault diagnosis method is mainly divided into two steps. The first step is EMD decomposition of vibration signal, and the second step is to classify rolling bearing faults by using BP neural network optimized by Double Strategy Particle Swarm Optimization algorithm. Experiments show that DSPSOBP has stronger advantages than BP neural network basic fault diagnosis model.

Keywords: Bearing fault diagnosis, BP neural network, optimization algorithm, particle swarm optimization

DOI: 10.3233/JIFS-213485

Journal: Journal of Intelligent & Fuzzy Systems, vol. 43, no. 5, pp. 5965-5971, 2022