A Multi-Source Consistency Domain Adaptation Neural Network MCDANN for Fault Diagnosis

Abstract

As the complexity and cost of industrial systems continue to increase, so does the need for the safety and reliability of industrial systems. In recent years, in the field of mechanical fault diagnosis, methods based on deep learning are gradually gaining popularity. The traditional deep learning method assumes that the training set and the test set belong to the same working condition, which is contrary to the actual industrial process. In order to improve the general ability of the fault diagnosis model, researchers start to study the domain adaptation method. However, most domain adaptation methods do not impose constraints on the test set, which leads to the occurrence of the domain mismatch problem. This paper proposes a multi-source consistency domain adaptation neural network MCDANN, which uses sub-domain division alignment and multi-source prediction consistency to achieve fine-grained domain matching and improve the transfer accuracy of the model. This paper conducts domain adaptation experiments on the open-source bearing fault dataset CWRU and DIRG bearing dataset and compares them with other classical methods. Experiments show that in the case of a signal-to-noise ratio of −4, the MCDANN model achieves an average diagnostic accuracy of more than 96% on the CWRU dataset and the DIRG dataset on noisy fault signals from the target domain, and is superior in almost all fields than other adaptive models.