基于VMD灰度图像编码和CNN的多传感融合轴承故障诊断

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摘要针对滚动轴承振动信号非平稳、非线性且易受噪声干扰的特点，以及单一振动信号对某些轴承故障识别率偏低的问题，提出一种基于VMD灰度图像编码和CNN的多传感融合轴承故障诊断方法。首先，采用VMD对驱动端和风扇端振动信号分解，提取各阶本征模态分量与原始信号相关系数最大的分量；其次，将筛选出的IMF分量依次排列并转换成灰度图像；最后，设计CNN结构，将训练集输入网络进行训练，测试集验证网络的有效性，实现滚动轴承故障识别。CWRU数据集和XJTU-SY数据集测试准确率分别达到99.90%和100%，结果表明，该方法能够准确识别变工况下轴承故障类别及损伤程度；对原始信号加入高斯噪声后的测试准确率分别达到99.75%和99.90%，证明该方法具有良好的泛化能力和抗噪性能。

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	崔桂艳1
	钟倩文1
	郑树彬1
	彭乐乐1
	文静1
	丁亚琦2

关键词 ：故障诊断, 信息融合, 变分模态分解（VMD）, 卷积神经网络, 灰度图像

Abstract：Rolling bearing vibration signals have the characteristics of non-stationary, nonlinear, vulnerable to noise, and the low recognition rate of a single vibration signal to some bearing faults, a multi-sensor fusion bearing fault diagnosis method based on VMD gray image coding and CNN was proposed. Firstly, the vibration signals of the drive end and the fan end were decomposed by VMD to extract the intrinsic mode components with the largest correlation coefficient. Secondly, the selected IMF components were arranged in turn and converted into grayscale images. Finally, the CNN was trained by the training set, the effectiveness of the network was verified by the testing set to identify the fault types of bearings. The test accuracy of the CWRU data set and XJTU-SY data set is 99.90% and 100% respectively, the results show that this method can accurately identify bearing fault types and damage degree under variable working conditions. After adding Gaussian noise to the original signal, the test accuracy is 99.75% and 99.90% respectively, which proves that the method has good generalization ability and anti-noise performance.

Key words： fault diagnosis information fusion variational mode decomposition (VMD) convolutional neural network gray image

收稿日期: 2021-11-01 出版日期: 2023-11-15

引用本文:

崔桂艳1，钟倩文1，郑树彬1，彭乐乐1，文静1，丁亚琦2. 基于VMD灰度图像编码和CNN的多传感融合轴承故障诊断[J]. 振动与冲击, 2023, 42(21): 316-326.
CUI Guiyan1, ZHONG Qianwen1, ZHENG Shubin1, PENG Lele1, WEN Jing1, DING Yaqi2. Multi-sensor fusion bearing fault diagnosis based on VMD gray level image coding and CNN. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(21): 316-326.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2023/V42/I21/316

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