Bearing Fault Diagnosis in the Mixed Domain Based on Crossover-Mutation Chaotic Particle Swarm

Tongle Xu,Junqing Ji,Xiaojia Kong,Wilson Wang,Fanghao Zou,Hocine Cherifi

doi:10.1155/2021/6632187

Abstract

The classification frameworks for fault diagnosis of rolling element bearings in rotating machinery are mostly based on analysis in a single time-frequency domain, where sensitive features are not completely extracted. To solve this problem, a new fault diagnosis technique is proposed in the mixed domain, based on the crossover-mutation chaotic particle swarm optimization support vector machine. Firstly, fault features are generated using techniques in the time domain, the frequency domain, and the time-frequency domain. Secondly, the weighted maximum relevance minimum redundancy (WMRMR) algorithm is adopted to reduce the dimension of the feature set and to establish the representative feature set. Thirdly, a new crossover-mutation strategy is suggested to reduce the local minima in optimization, and an optimization disturbance is added. Finally, the support vector machine is optimized using the improved chaotic particle swarm to improve fault classification diagnosis. The effectiveness of the proposed new bearing fault diagnostic technique is verified by experimental tests under different bearing conditions. Test results showed that the bearing fault classification accuracy of CMCPSO-SVM in the mixed domain was much higher than those in a single feature domain.

Highlights

E above fault diagnosis techniques mainly focused on time-frequency characteristics of the decomposed signal, and the characteristics of the original signal in the time domain and the frequency domain were usually ignored
In order to solve the problem of sensitive features incomplete extraction in time-frequency domain, a fault diagnosis method based on crossover-mutation chaotic particle swarm optimization support vector machine in the mixed domain is proposed in this paper. e innovation points of this paper are as follows: (1) e sensitive characteristic values of the fault signals are selected in mixed domain, and the weighted maximum relevance minimum redundancy (WMRMR) algorithm is used to reduce the dimension of the sensitive feature set to obtain the optimal low-dimensional feature subset
(2) A new cross-mutation chaotic particle swarm optimization (CMCPSO)-SVM classifier is proposed. e new crossover-mutation strategy and an optimization disturbance are added to the chaotic particle swarm optimization (CPSO) algorithm to avoid the local optimization and improve the classification accuracy

Summary

Extract Mixed Domain Features

In order to extract fault features in the time-frequency domain more accurately and effectively, a dual-LMD morphological filtering method is adopted [27]. Bearing fault feature information is mainly concentrated in the first five PF components, so the first five PF component permutation entropies will be extracted as the time-frequency domain set, which is written as [TF21, . Based on the crossover-mutation process of genetic algorithm, a new adaptive crossover-mutation strategy [36] is adopted to improve the chaos particle swarm optimization in this paper. (1) Initialize chaotic particle swarm parameters, including inertial weight w, self-learning and social learning weight coefficients c1 and c2, maximum number of iteration kmax, swarm size, optimization disturbance χ, penalty factor p, and parameter θ. (5) Optimize p and θ in the SVM using the optimal parameters obtained in steps (3) and (4), and construct the CMCPSO-SVM model

Performance Evaluation for Bearing Fault Diagnosis

Findings

Conclusion