基于变分模态分解与深度卷积神经网络的滚动轴承故障诊断

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摘要

针对滚动轴承振动信号非平稳、非线性特点以及特征提取困难问题，提出一种基于变分模态分解（VMD）与深度卷积神经网络相结合的特征提取方法并应用于滚动轴承故障诊断。利用VMD将原始振动信号分解得到若干不同频率的限带本征模态分量，通过卷积网络中的多组卷积核自动学习各模态数据的不同特征，保证了特征提取的自适应性、全面性和多样性。在特征提取的基础上，使用全连接神经网络进行故障分类与诊断。将所提方法应用于滚动轴承故障诊断，结果表明，该方法在变工况情况下能够实现滚动轴承故障类别以及损伤程度的精确判定。

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	丁承君1
	冯玉伯1
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	王曼娜1

关键词 ：变分模态分解(VMD), 深度卷积神经网络, 特征提取, 智能故障诊断, 滚动轴承

Abstract：Rolling bearing vibration signals have the characteristics of non-stationary and nonlinear，and it is difficult to extract features.So a method of rolling bearing fault diagnosis using variational mode decomposition (VMD) and deep convolutional neural network (CNN) was proposed in this paper.The original vibration signal was decomposed into several intrinsic mode function (IMF) with different frequencies, the different features of each modal data were automatically learned by convolutional kernels in the CNN, which ensured the self-adaptation and comprehensiveness and diversity of feature extraction.Full-connected neural networks were used for fault classification and diagnosis on the basis of feature extraction.The results show that this method can accurately identify the type of rolling bearing fault and the degree of damage under changing conditions.

Key words： variational mode decomposition(VMD) deep convolutional neural network feature extraction intelligent fault diagnosis rolling bearing

收稿日期: 2018-10-09 出版日期: 2021-01-28

引用本文:

丁承君1，冯玉伯1,2，王曼娜1. 基于变分模态分解与深度卷积神经网络的滚动轴承故障诊断[J]. 振动与冲击, 2021, 40(2): 287-296.
DING Chengjun1, FENG Yubo1,2, WANG Manna1. Rolling bearing fault diagnosis using variational mode decomposition and deep convolutional neural network. JOURNAL OF VIBRATION AND SHOCK, 2021, 40(2): 287-296.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2021/V40/I2/287

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