基于卷积神经网络的领域适配模型的多工况迁移的轴承故障诊断

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Abstract In view of the fact that the deep learning model trained under single working condition data can't realize effective fault diagnosis under complex working conditions, a convolutional neural network-domain adaptation model (CNN-DA) is proposed. Convolution network is used to extract high-level features of fault vibration signals. Channel Attention Mechanism (CAM) is added to the network at the beginning and end to dynamically allocate the weights of feature channels and reduce the interference of invalid information. Combining with the domain adaptive method, the high-level fault features obtained by the feature extraction layer are adapted in the Source and Target domains. The domain adaptation module integrates the Whole Domain Adaptation and the Category Domain Adaptation, so that the data distribution of the features of the same fault tag in the two domains tends to coincide gradually. Finally, the deep learning model is applied to a variety of different situations for training, and the training results and test results are obtained. Through experiments on data sets from different sources, the model is tested under various working conditions, and the results show that the proposed model can cope with the fault detection of rolling bearings under complex working conditions.

Key words： Fault Diagnosis Deep Learning convolutional neural network—domain adaptation（CNN-DA） Domain Adaptation

Received: 17 September 2021 Published: 28 December 2022

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	Articles by authors
	QIAN Siyu
	QIN Dongchen
	CHEN Jiangyi
	YUAN Feng

Cite this article:

QIAN Siyu,QIN Dongchen,CHEN Jiangyi, et al. Bearing fault diagnosis based on a domain adaptation model of convolutional neural network under multiple working conditions[J]. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(24): 192-200.

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http://jvs.sjtu.edu.cn/EN/ OR http://jvs.sjtu.edu.cn/EN/Y2022/V41/I24/192

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