Fault Diagnosis of Bearing Based on G-DPSO and Decision Tree

doi:10.3969/j.issn.1007-7375.2021.06.006

Abstract

Abstract: In view of the limitation of feature extraction in condition monitoring and fault diagnosis technology of mechanical equipment on the accuracy of diagnosis, as much useful information as possible can be extracted from the original fault signal data. It is proposed to diagnose and analyze the bearing fault by using the best feature data set, and extract the feature from the fault data in the amplitude and frequency domains, respectively. An improved particle swarm optimization (G-DPSO) algorithm is used to screen the extracted feature data sets, optimize the weight coefficients of the traditional particle swarm optimization algorithm, and combine it with the information entropy increase of decision tree model for fault diagnosis. It can extract the most suitable feature vectors for fault diagnosis. Five kinds of bearing fault data are used to test and analyze the proposed method. The diagnostic accuracy can reach above 97%, which proves that the proposed method is effective and reliable.

Key words: bearing, feature extraction, G-DPSO, decision tree, fault diagnosis

CLC Number:

TP206+.3

ZHANG Yanliang, YAN Jianyong. Fault Diagnosis of Bearing Based on G-DPSO and Decision Tree[J]. Industrial Engineering Journal, 2021, 24(6): 41-47.

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