Fault Diagnosis for Rolling Bearings Based on CNN-SN and Unsupervised Domain Adaptation
Aimed at the problem that the difference in distribution of vibration data of rolling bearings under different working conditions is large and it is difficult to obtain all fault label samples,resulting in poor generalization ability of fault diagnosis model,a fault diagnosis method under variable working conditions is proposed based on convolutional neural networks-shrink network(CNN-SN)and unsupervised domain adaptation.Firstly,a domain shared one-dimensional convolutional neural network is constructed to extract fault features from vibration signals,a soft threshold learning mechanism is introduced to construct a local feature shrink network,and the impact of noise on fault feature extraction is mitigated.Then,the regularization constraint of maximum mean discrepancy is introduced to fault features extracted from samples under different working conditions,and the global alignment of features in source domain and target domain is achieved.Finally,for unlabeled target condition samples,the adversarial learning strategy of maximizing and minimizing difference of classifiers is used to achieve finer grained sub domain alignment of different domain features.The bearing data set from Jiangnan University is used to verify the proposed method.The results show that the proposed method has good domain adaptability and high cross domain fault diagnosis accuracy.
rolling bearingfault diagnosisvariable working conditiontransfer learningunsupervised domain adaptation
NETL
NSTL
万方数据
