重参数化VGG网络在滚动轴承故障诊断中的应用研究

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摘要基于神经网络的滚动轴承故障诊断方法训练时，存在诊断准确率低和易受到变工况噪声干扰的问题，提出一种基于重参数化VGG（RepVGG）滚动轴承故障诊断方法。首先，为满足神经网络对数据量的要求，采用数据增强技术来扩充原始数据，然后，使用短时傅里叶变换（STFT）对原始的振动信号处理成单通道时频图，并使用伪彩色处理技术转换成三通道时频图，进一步将数据输入到RepVGG网络的不同结构中进行滚动轴承的故障诊断。最后，在凯斯西储大学滚动轴承数据集（CWRU）上开展实验验证，实验结果表明，RepVGG在变工况及噪声干扰下的平均诊断准确率分别为98.02%、95%以上，高于基于VGG、ResNet的故障诊断模型，有较高的故障诊断准确率且泛化性更好。

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	丁汕汕1
	陈仁文1
	黄翊君1
	刘飞1
	刘昊1
	肖安2

关键词 ：轴承故障诊断, RepVGG, 数据增强, 泛化性

Abstract：Training time of neural network based rolling bearing fault diagnosis method, there are problems of low diagnostic accuracy and susceptibility to interference from variable operating noise, a rolling bearing fault diagnosis method based on reparameterized VGG (RepVGG) is proposed to solve these problems. Firstly, to meet the data volume requirement of the neural network, the data enhancement technique is used to expand the original data. Then, the original vibration signal is processed into a single-channel time-frequency map using the short-time Fourier transform (STFT) and converted into a three-channel time-frequency map using the pseudo-color processing technique, and the data are further input into different structures of the RepVGG network for the fault diagnosis of rolling bearings. Finally, experimental validation is carried out on the rolling bearing dataset (CWRU) of Case Western Reserve University, and the experimental results show that the average diagnostic accuracy of RepVGG under variable operating conditions and noise interference is 98.02% and over 95%, respectively, which is higher than that of VGG and ResNet fault diagnosis models, with higher fault diagnosis accuracy and stronger generalization.

Key words： Bearing fault diagnosis RepVGG Data enhancement Generalizability

收稿日期: 2022-05-23 出版日期: 2023-06-15

引用本文:

丁汕汕1，陈仁文1，黄翊君1，刘飞1，刘昊1，肖安2. 重参数化VGG网络在滚动轴承故障诊断中的应用研究[J]. 振动与冲击, 2023, 42(11): 313-323.
DING Shanshan1， CHEN Renwen1， HUANG Yijun1， LIU Fei1， LIU Hao1， XIAO An2. Application study of reparameterized VGG network in rolling bearing fault diagnosis. JOURNAL OF VIBRATION AND SHOCK, 2023, 42(11): 313-323.

链接本文:

http://jvs.sjtu.edu.cn/CN/ 或 http://jvs.sjtu.edu.cn/CN/Y2023/V42/I11/313

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