Rolling bearing intelligent fault diagnosis method based on IPSO-WCNN

Abstract

In the bearing fault diagnosis process using the convolution neural network (CNN), there are some problems, such as complex signal data processing and the complex network parameter setting. A rolling bearing fault diagnosis method is proposed to solve these problems based on improved particle swarm optimization and convolution neural networks with wide kernels in first-layer (IPSO-WCNN). The particle self-adaptive jump out algorithm is proposed to overcome particle swarm optimization (PSO) shortcomings. The adaptive inertia weight and the linear change acceleration coefficients are adopted for improved particle swarm optimization (IPSO). The convolution neural networks with wide kernels in first-layer (WCNN) fault diagnosis method is proposed for one-dimensional rolling bearing vibration signals, and the parameters of the WCNN is optimised by IPSO. According to the verification experiments, the proposed method can get higher accuracy than others with good adaptability.