Multisource weighted domain adaptive network for bearing fault diagnosis

Bearing failure is one of the common faults in rotating mechanical systems, significantly impacting the reliability and performance of the machinery. However, due to variations in operating conditions and environmental factors, bearing failure data exhibits a multi-source domain issue, posing challenges for traditional fault diagnosis methods to be effectively applied. In this paper, a new multi-source weighted domain adaptive (MSWDA) framework is proposed, which is implemented by feature weighted and multi-layer feature extraction structure. To achieve feature-level distribution alignment, the minimum slice Wasserstein distance is used as a measure to measure the difference in feature distribution. Extensive experiments are conducted on the CWRU bearing datasets to validate the effectiveness of the proposed model.