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Fault diagnosis method of rolling bearing based on adaptive redundancy matrix classifier
Published:2023-05-25 author:LIU Zhen-hua, HU Si-yuan, ZHAO Jie. Browse: 1204 Check PDF documents
Fault diagnosis method of rolling bearing based on adaptive 
redundancy matrix classifier


LIU Zhen-hua1, HU Si-yuan2, ZHAO Jie3

(1.Department of Mechanical and Electrical Engineering, Xinzhou Vocational and Technical College, 

Xinzhou 034000, China; 2.School of Mechanical Engineering, Shandong University of Technology, 

Zibo 255000, China; 3.Shandong Yaohua Tenai Technology Co., Ltd., Binzhou 256619, China)


Abstract:  There are some limitations when using traditional matrix classifier(support matrix machine, SMM)to diagnose rolling bearing faults, that is, it is difficult to extract effective features for modeling when classifying redundant features. Therefore, a rolling bearing fault diagnosis method based on adaptive redundancy matrix classifier (ARMC) was proposed.Firstly, in the process of constructing ARMC model, the kernel function was used to create a high-dimensional distribution space to solve the problem of linear indivisibility of sample data. Then, the idea of constrained L1 norm was used to minimize the distance from the sample to all clustering convex hull boundaries, and it was transformed into a problem of solving linear programming, which reduced the model computational complexity.The low rank term was controlled by regularization constraint, and the influence of redundant features and noise components on the model was weakened to obtain a more accurate prediction model. Finally,in order to verify the effectiveness of the ARMC method, the rolling bearing experiment data of Case Western Reserve University(CWRU) and the data of the self-made rolling bearing fault simulation experiment platform were used to conduct experiments respectively. The results obtained by the this method were compared with those obtained by the other methods.The results show that L1 norm and kernel function were used to construct and solve the objective function in the ARMC, which can not only protect the structural information of the object to be diagnosed, but also weaken the complexity of the model and enhance the robustness of the model. Comparing with support matrix machine(SMM) and robust support matrix machine, ARMC can fully consider the problem of weakening the redundant information of samples, and the average recognition rate is improved by 3%~8%.

Key words:  adaptive redundant matrix classifier(ARMC); support matrix machine (SMM); high-dimensional distribution space; redundant feature classification; model robustness; kernel function

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