样本不均衡时轴承故障的非平衡卷积网络诊断  

Unbalanced convolution network diagnosis of bearing faults under imbalanced samples condition

作　　者：裴红蕾[1] PEI Honglei(School of Electromechanical and Information Engineering,Wuxi Vocational Institute of Arts&Technology,Yixing 214200,CHN)

机构地区：[1]无锡工艺职业技术学院机电与信息工程学院,江苏宜兴214200

出　　处：《制造技术与机床》2023年第4期174-180,共7页Manufacturing Technology & Machine Tool

基　　金：年度宜兴市科技创新专项科技攻关项目(2021SF04)。

摘　　要：为了提高样本不均衡条件下轴承故障诊断精度,提出了基于VAE-SNN的样本增广方法和基于非平衡损失网络的故障诊断方法。首先,使用变分自编码器用于数据生成,并依据孪生神经网络对生成数据的类别进行判定,实现了基于变分自编码器和孪生神经网络的样本增广;其次,分析了卷积神经网络无差别对待样本的缺点,针对不均衡样本的特殊性,提出了非平衡损失函数卷积网络,该网络能够自动关注数量少、难分的样本训练。经实验验证,生成对抗网络增广的样本相似度为0.847,孪生神经网络增广的样本相似度比对抗网络提高了6.61%,说明孪生神经网络的样本增广效果更好;在相同诊断方法前提下,样本增广后比增广前的准确率提高了9.42%,说明样本增广有利于提高轴承的故障诊断准确率;非均衡损失网络比卷积神经网络的诊断精度提高了7.17%,比自适应深度学习提高了4.12%,验证了非均衡损失网络的高准确率和优越性。In order to improve the accuracy of bearing fault diagnosis under the condition of unbalanced samples,a sample expansion method based on VAE-SNN and a fault diagnosis method based on unbalanced loss network are proposed.Firstly,the variational self-coder is used to generate data,and the category of generated data is determined according to the twin neural network.The sample expansion based on the variational self-coder and the twin neural network is realized;Secondly,the disadvantages of convolutional neural network are analyzed.Aiming at the particularity of unbalanced samples,an unbalance loss function convolutional network is proposed,which can automatically focus on the training of small and difficult samples.The experimental results show that the similarity of the expanded samples of the generated countermeasure network is 0.847,and the similarity of the expanded samples of the twin neural network is 6.61%higher than that of the countermeasure network,indicating that the sample expansion effect of the twin neural network is better;On the premise of the same diagnosis method,the accuracy rate of the expanded sample is increased by 9.42%compared with that before the expansion,which indicates that the expanded sample is useful to improve the accuracy rate of bearing fault diagnosis;The diagnosis accuracy of unbalanced loss network is 7.17%higher than that of convolutional neural network and 4.12%higher than that of adaptive deep learning,which verifies the high accuracy and superiority of unbalanced loss network.

关 键 词：样本不均衡 轴承故障诊断 非平衡损失函数 孪生神经网络 变分自编码器 

分 类 号：TH133[机械工程—机械制造及自动化] TP391[自动化与计算机技术—计算机应用技术]