Electrical Machine Bearing Fault Diagnosis Based on Deep Gaussian Process Optimized by Particle Swarm

Abstract

Aiming at the problems of low accuracy and slow diagnosis speed in the existing fault diagnosis model of electrical machine bearing, this paper presents an electrical machine bearing fault diagnosis method based on Deep Gaussian Process of particle swarm optimization(DGP). A total of 10 characteristics of 9 damage states and no fault states of the bearing are determined， constructing a deep Gaussian process model for electrical machine bearing fault diagnosis based on expectation propagation and Monte Carlo method, and use the particle swarm optimization algorithm to perform parameter searching optimization for its induction point value. The experimental results show that the fault recognition rate of DGP on the CWRU data set reaches 95%, significantly better than other deep learning methods, integration methods and machine learning methods. DGP method can better diagnose electrical machine bearing faults, provide technical support for the safe operation of the electrical machine which are important for real industrial applications.