机械科学与技术2024，Vol.43Issue(5)：773-780,8.DOI:10.13433/j.cnki.1003-8728.20230036
结合SE-VAE与M1DCNN的小样本数据下轴承故障诊断
Bearing Fault Diagnosis Under Small Sample Data Based on SE-VAE and M1DCNN
摘要
Abstract
Aiming at the problem of low diagnostic accuracy caused by the small number of fault samples in bearing fault diagnosis,a new bearing fault diagnosis method based on attention mechanism variation autoencoder(SE-VAE)and multi-scale one-dimensional convolutional neural network(M1DCNN)was proposed.Firstly,the training set of bearing data set is input into SE-VAE for training,generated samples with similar distribution to the training samples are obtained and added to the training set to increase the number of samples in the training set.Then,the extended training set is input into M1DCNN for training,and finally the trained model is applied to the test set to output the fault diagnosis results.Experimental results show that the proposed method can achieve better fault diagnosis accuracy on small sample bearing fault data sets with different loads.关键词
轴承故障诊断/变分自编码器/注意力机制/多尺度一维卷积神经网络/小样本Key words
bearing fault diagnosis/variation autoencoder(VAE)/attention mechanism/multiscale one-dimensional convolutional neural network/small sample分类
信息技术与安全科学引用本文复制引用
李梦男,李琨,叶震,高宏宇..结合SE-VAE与M1DCNN的小样本数据下轴承故障诊断[J].机械科学与技术,2024,43(5):773-780,8.基金项目
国家自然科学基金项目(82160787)与昆明理工大学科技园有限公司下达项目(2018KF3) （82160787）
