Bearing fault diagnosis based on improved particle swarm optimized VMD and SVM models

Qingfeng Zhang,Zhan Peng Fan,Shuang Chen

doi:10.1177/16878140211028451

Abstract

To improve the accuracy of fault diagnosis of bearing, the improved particle swarm optimization variational mode decomposition (VMD) and support vector machine (SVM) models are proposed. Aiming at the convergence effect of particle swarm optimization (PSO), dynamic inertia weight, and gradient information are introduced to improve PSO (IPSO). IPSO is used to optimize the optimal number of VMD modal components and the penalty factor, which is applied to the vibration signal decomposition. The fault sample set is constructed by calculating the multi-scale information entropy of each component signal obtained from the bearing vibration signals. At the same time, IPSO is used to optimize the support vector machine (IPSO-SVM), which is used to bearing fault diagnosis. The time-domain feature data set is used as the comparison data set, and the classical PSO, genetic algorithm, and cross-validation method are used as the comparison algorithm to verify the effectiveness of the method in this paper. The research results show that the optimized VMD can effectively decompose the vibration signal and can effectively highlight the fault characteristics. IPSO can increase the accuracy by 2% without adding additional costs. And the accuracy, volatility, and convergence error of IPSO are better than comparison algorithms.

Highlights

The rotor system in large rotating machinery such as aero-engine is the power source of the entire machinery, and its supporting points are very prone to failure when working under alternating load conditions all year round
The performance state of the bearing determines whether the rotor system can operate stably, and the traditional oil analysis method as one of the means of detecting the performance state of the industrial bearing, its technical means have gradually matured.[2]
Because the results of the oil analysis method are affected by the bearing failure status, inspectors, etc., and cannot detect early bearing failures, Mingfu et al.[3] conducted research based on rotor dynamics and found that the vibration characteristics of the machinery can reflect the performance of the machinery status

Summary

Introduction

The rotor system in large rotating machinery such as aero-engine is the power source of the entire machinery, and its supporting points are very prone to failure when working under alternating load conditions all year round. The vibration signal is decomposed by optimized VMD, and the multi-scale entropy of each component is calculated to construct the fault sample data set.

Results

Conclusion