Bearing fault diagnosis based on a domain adaptation model of convolutional neural network under multiple working conditions

In view of the fact that the drop learning model trained under single working condition data cannot realize effective fault diagnosis under complex working conditions, a convolutional neural network-domain adaptation model (CNN-DA) was proposed. The convolution network was used to extract high-level features of fault vibration signals. Channel attention mechanism (CAM) was 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 were 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 was applied to a variety of different situations for training, and the training results and test results were obtained. Through experiments on data sets from different sources, the model was 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. © 2022 Chinese Vibration Engineering Society. All rights reserved.