基于卷积神经网络图像分类的轴承故障模式识别

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Abstract Conventional machinery fault pattern recognition methods based on data-driven need to construct algorithms manually to extract characteristics. Manually constructed feature extraction algorithms are complicated. In order to solve the problems , a bearing fault pattern recognition method based on image classification with CNN was proposed, with the consideration of the wide application of CNN in image feature automatic extraction and classification. Firstly, with the EEMD method, bearing vibration signals were decomposed and a finite number of intrinsic mode functions(IMF) were selected according to the criterion of correlation coefficient. Secondly, with the PWVD method, time-frequency diagrams were obtained from selected IMFs and preprocessing was carried on time-frequency diagrams. Lastly, fifteen kinds of preprocessed bearing time-frequency diagrams served as import of CNN for feature extraction and classification. Comparison was made between this methodology and another similar method, and the classification accuracy improved by 4.26%.

Key words： ensemble empirical mode decomposition(EEMD) pseudo Wigner-Ville time-frequency distribution(PWVD) convolutional neural network(CNN) image classification bearing pattern recognition

Received: 31 July 2018 Published: 15 February 2020

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	Articles by authors
	ZHANG An’an1
	HUANG Jinying1
	JI Shuwei2
	LI Dong1

Cite this article:

ZHANG An’an1,HUANG Jinying1,JI Shuwei2, et al. Bearing fault pattern recognition based on image classification with CNN[J]. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(4): 165-171.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2020/V39/I4/165

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