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Fault diagnosis method of rolling bearing based on relevance support matrix machine

Published:2022-02-23 author:CHEN Ying, CHEN Mu-rong. Browse: 701 Check PDF documents

Fault diagnosis method of rolling bearing based on

relevance support matrix machine

CHEN Ying1, CHEN Mu-rong2,3

(1.School of Electronic Information Engineering Changsha Social Work College,Changsha 410004, China;

2.School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510640, China;

3.Department of mechanical and electrical engineering Guangdong Polytechnic College, Zhaoqing 526100, China)

Abstract: In order to solve the problem that support matrix machine (SMM) lacked necessary probability information in classification modeling, resulting in its unclear sparsity and robustness, a new matrix classifier called relevance support matrix machine (RSMM) was proposed according to Bayesian theory framework. In RSMM, firstly the multi-classification objective function was established with the matrix as the modeling element, the structured information between rows and columns of the input matrix was made full use of to establish accurate prediction model. Then, Bayesian learning framework was used to impose a conditional probability distribution constraint on model parameters to obtain sparse solution space. The kernel function of the RSMM method was not restricted by Mercer condition, and the probability and statistical information between different categories could be obtained.The prior probability was introduced into the model weight setting to make the RSMM model have the feature of sparsity, and the uncertain samples were classified. Finally, the rolling bearing fault classification experiment was carried out, and the rolling bearing data set was used to test the performance of this method.The results show that RSMM can accurately classify uncertain samples by using Bayesian learning framework and a prior probability. At the same time, RSMM can make full use of the structured information of samples.Comparing with SMM and its improved algorithms, RSMM improves the overall recognition rate by 2%~8%, which proves that RSMM has superior classification performance in roller bearing fault diagnosis.

Key words: rolling bearing; fault diagnosis; relevance support matrix machine(RSMM); Bayesian framework

CHEN Ying, CHEN Mu-rong. Fault diagnosis method of rolling bearing based on relevance support matrix machine［J］.Journal of Mechanical & Electrical Engineering, 2021,38(12):1592-1598.

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