基于1D-LeNet-5模型的滚动轴承故障诊断方法

兰州理工大学学报 ›› 2023, Vol. 49 ›› Issue (5): 34-41.

基于1D-LeNet-5模型的滚动轴承故障诊断方法

郭俊锋^*, 孙磊, 王淼生, 续德锋

兰州理工大学机电工程学院, 甘肃兰州 730050

收稿日期:2022-04-06 出版日期:2023-10-28 发布日期:2023-11-07
通讯作者: 郭俊锋(1978-),山西临汾人,博士,教授. Email:junf_guo@163.com
基金资助:
国家自然科学基金(51465034)

Fault diagnosis method of rolling bearing based on 1D-LeNet-5 model

GUO Jun-feng, SUN Lei, WANG Miao-sheng, XU De-feng

School of Mechanical and Electrical Engineering, Lanzhou Univ. of Tech., Lanzhou 730050, China

Received:2022-04-06 Online:2023-10-28 Published:2023-11-07

摘要/Abstract

摘要： 风力发电过程中,轴承能否正常运行关系到风电机组能否正常工作.针对现有基于深度学习的轴承故障诊断模型结构复杂、参数众多和训练困难的问题,提出了基于LeNet-5模型改进的一维卷积神经网络滚动轴承故障诊断方法.首先,为了更大程度提取故障信息,引入短时傅里叶变换对原始振动信号进行预处理.其次,设计一维网络模型,其感受野更大,计算速度更快;同时,引入Leaky-ReLU激活函数,其对输入信号的细节处理能力更强;并且增加批归一化层和Dropout层,提高模型泛化能力.最后,利用训练后的模型进行故障诊断实验.结果表明,该方法在10类轴承故障分类中诊断准确率能够达到99.98%,针对风电机组轴承故障诊断具有较好的工程应用前景.

关键词: 风电机组, 滚动轴承, 故障诊断, 卷积神经网络, 短时傅里叶变换, LeNet-5

Abstract: In the process of wind power generation, the normal operation of the bearing is related to the normal operation of the wind turbine. Aiming at the problem that the existing deep learning-based bearing fault diagnosis model has a complex structure and many parameters, which makes it difficult to train the model, an improved one-dimensional convolutional neural network rolling bearing fault diagnosis method based on the LeNet-5 model is proposed. First, in order to extract fault information to a greater extent, a short-time Fourier transform is introduced to preprocess the original vibration signal. Secondly, a one-dimensional network model is designed, which has a larger receptive field and faster calculation speed. At the same time, the Leaky-ReLU activation function is introduced to make the ability to process the details of the input signal stronger. The batch normalization layer and Dropout layer are added to improve the model generalization ability. Finally, the trained model is used to perform fault diagnosis experiments. The experimental results show that the diagnostic accuracy of this method can reach 99.98% in the classification of ten types of bearing faults, which has a good engineering application prospect for the fault diagnosis of wind turbine bearings.

Key words: wind turbine, rolling bearing, fault diagnosis, convolutional neural networks, short-time fourier transform, LeNet-5

中图分类号:

TH17

郭俊锋, 孙磊, 王淼生, 续德锋. 基于1D-LeNet-5模型的滚动轴承故障诊断方法[J]. 兰州理工大学学报, 2023, 49(5): 34-41.

GUO Jun-feng, SUN Lei, WANG Miao-sheng, XU De-feng. Fault diagnosis method of rolling bearing based on 1D-LeNet-5 model[J]. Journal of Lanzhou University of Technology, 2023, 49(5): 34-41.

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