Multi-layer adaptive convolutional neural network unsupervised domain adaptive bearing fault diagnosis method

Abstract Deep learning provides a feasible fault diagnosis method for intelligent mechanical systems. However, this method requires a large amount of marking data, which greatly limits its application in the actual industry. Therefore, this paper proposes a multi-layer adaptive convolutional neural network unsupervised domain adaptive bearing fault diagnosis method (MACNN), which is especially suitable for bearing fault classification under variable working conditions. First, a new method to improve domain alignment is proposed (LD-CORAL). This method uses Log-Euclidean distance to measure deep coral loss, which solves the problem that the covariance matrix cannot be aligned correctly in the manifold structure. Then, it proposes multi-layer adaptation of LD-CORAL loss in the fully connected layer, and combines center-based discriminative loss to improve the feature learning ability of the model, which can improve the classification accuracy and domain adaptation performance of the model. Finally, in order to verify the effectiveness and feasibility of the proposed method, the method is applied to the multi-fault diagnosis of gearbox bearings under variable working conditions. Comparing the classification results of different methods, the conclusion shows that this method is more effective for bearing fault classification under variable working conditions.