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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摘要针对传统的基于数据驱动的机械故障模式识别方法中需要人工构造算法提取特征以及人工构造特征提取算法繁琐的问题，结合卷积神经网络(CNN)在图像特征自动提取与图像分类识别中的广泛应用，提出了一种基于CNN图像分类的轴承故障模式识别方法。首先，利用集合经验模态分解(EEMD)方法对轴承振动信号进行自适应分解并用相关系数对得到的本征模函数分量进行筛选。其次，对筛选得到的本征模函数分量进行伪魏格纳-威利时频分析（PWVD）计算得到信号的时频分布图，并对时频图进行预处理。最后，将轴承15种不同工况预处理后的时频图利用CNN进行特征提取与分类识别。将该方法与同类方法进行了对比，分类正确率提高了4.26%。

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	张安安1
	黄晋英1
	冀树伟2
	李东1

关键词 ：集合经验模态分解（EEMD）, 伪魏格纳-威利时频分析（PWVD）, 卷积神经网格（CNN）, 图像分类, 轴承, 模式识别

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

收稿日期: 2018-07-31 出版日期: 2020-02-15

引用本文:

张安安1，黄晋英1，冀树伟2，李东1. 基于卷积神经网络图像分类的轴承故障模式识别[J]. 振动与冲击, 2020, 39(4): 165-171.
ZHANG An’an1,HUANG Jinying1,JI Shuwei2,LI Dong1. Bearing fault pattern recognition based on image classification with CNN. JOURNAL OF VIBRATION AND SHOCK, 2020, 39(4): 165-171.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2020/V39/I4/165

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