A novel fault diagnosis model of rolling bearing under variable working conditions based on attention mechanism and domain adversarial neural network

Deep learning has been used to enhance the efficiency of rolling bearing fault diagnosis. However, the complexity of working conditions in rolling bearings, coupled with varying data distribution, often results in the failure of most deep learning models. To solve this problem, a feature extractor with an attention mechanism is constructed, which allows the model to selectively study and preserve critical features relevant to fault information during the training process. Moreover, the Wasserstein distance is employed in adversarial neural networks to calculate the distribution discrepancy between data from different domains, which can avoid disappearing gradients or vanishing problems of the model. The experimental validation are conducted using the rolling bearing datasets from CWRU and Jiangnan University (JNU). Compared with other existing models, the proposed model has better performance under variable conditions, and this demonstrates the superiority and accuracy of this model.