摘要
针对轴承振动信号易受负载不平衡干扰以及轴承故障样本量少的问题,提出了一种基于梯度惩罚Wasserstein距离生成对抗网络(WGAN-GP)和自注意力卷积神经网络(SeCNN)的轴承故障诊断方法。对轴承振动信号进行短时傅里叶变换,得到易于WGAN-GP处理的时频谱样本,分为训练集、验证集、测试集;将训练集输入到WGAN-GP中进行对抗训练,生成与训练样本分布相似的新样本,并添加到训练集中以扩充训练集;将扩充后的训练集输入到SeCNN中进行学习,并将训练好的模型应用于测试集,输出故障识别结果。对CUT-2平台负载不平衡轴承数据集进行分析,实验结果表明,所提方法能够准确有效地对轴承故障进行分类。
Aiming at the problems that the bearing vibration signals were easily disturbed by unbalanced load and the small number of bearing fault samples,a bearing fault diagnosis method based on WGAN-GP and SeCNN was proposed.The bearing vibration signals were processed by short-time Fourier transform to get the time-spectrum samples that were easy to be processed by WGAN-GP,which were divided into training set,validation set and test set.Then the training set was inputted into WGAN-GP for adversarial training,new samples were generated with similar distribution to the training samples,and added to the training set to expand the training set.The expanded training set was input into SeCNN for learning,and the trained model was applied to the test set and output the fault recognition results.The analysis of the CUT-2 platform unbalanced load bearing data was carried out,and the experimental results show that the proposed method may accurately and effectively classify the bearing faults.
作者
何强
唐向红
李传江
陆见光
陈家兑
HE Qiang;TANG Xianghong;LI Chuanjiang;LU Jianguang;CHEN Jiadui(Key Laboratory of Advanced Manufacturing Technology,Ministry of Education,Guizhou University,Guiyang,550025;School of Mechanical Engineering,Guizhou University,Guiyang,550025;State Key Laboratory of Public Big Data,Guizhou University,Guiyang,550025)
出处
《中国机械工程》
EI
CAS
CSCD
北大核心
2021年第10期1164-1171,1180,共9页
China Mechanical Engineering
基金
国家重点研发计划(2018AAA0101800)
贵州省科学技术基金(黔科合基础-ZK〔2021〕一般271)。
关键词
轴承故障诊断
负载不平衡
小样本
短时傅里叶变换
梯度惩罚Wasserstein距离生成对抗网络
自注意力卷积神经网络
bearing fault diagnosis
unbalanced load
small sample
short-time Fourier transform
gradient penalty Wasserstein generative adversarial network(WGAN-GP)
convolutional neural network with self-attention mechanism(SeCNN)
