Fault diagnosis of rolling bearing based on MPE and PSO-SVM

Home > Archive>Volume 44, Issue 21, 2021 >44-48

Fault diagnosis of rolling bearing based on MPE and PSO-SVM
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                        Diao NingkunDiao Ningkun
1.Hebei Provincial Collaborative Innovation Center of Large Construction Machinery Manufacturing, Shijiazhuang Tiedao University, Shijiazhuang 050043, China; 2.School of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
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Ma HuaixiangMa Huaixiang
1.Hebei Provincial Collaborative Innovation Center of Large Construction Machinery Manufacturing, Shijiazhuang Tiedao University, Shijiazhuang 050043, China; 2.School of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
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Wang Jinshi2Wang Jinshi
School of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
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Liu Shuai2Liu Shuai
School of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
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Affiliation:1.Hebei Provincial Collaborative Innovation Center of Large Construction Machinery Manufacturing, Shijiazhuang Tiedao University, Shijiazhuang 050043, China; 2.School of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, China
Clc Number:TH133.33+1;TP181
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Abstract:

Rolling bearing is one of the important parts of rotating machine. Aiming at the problem of rolling bearing fault diagnosis, this paper proposes an algorithm combining multiscale permutation entropy (MPE) and support vector machine(SVM) optimized by particle swarm optimization (PSO). The fault characteristics of the bearing fault data was obtained by the MPE method, fitting as a feature vector into the PSO-SVM model, using Case Western Reserve University bearing dataset for verification. It is found that this method can effectively identify the fault of the rolling bearing. This method is compared with the fault classification results obtained by combining the multi-scale permutation entropy with the traditional SVM method and the SVM method optimized by grid search. It is found that the method proposed in this paper has certain advantages in the efficiency and accuracy of rolling bearing fault diagnosis.

Key words:multiscale permutation entropy; particle swarm optimization; support vector machine; rolling bearings

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Online: July 08,2024
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