Multi-Source Domain Adaptation Fault Diagnosis Based on Enhanced Multi-Kernel Maximum Mean Discrepancy

Although deep learning-based intelligent fault diagnosis methods have garnered significant research interest, their impressive performance relies on the assumption that the training and test data share the same distribution. This constraint limits their effectiveness in scenarios where these distributions differ. Unsupervised domain adaptation techniques for fault diagnosis address this issue; however, most existing approaches focus solely on data from a single source domain, which overlooks the potential for leveraging multiple source domains to enhance diagnostic accuracy. In this paper, we propose a multi-source domain adaptation fault diagnosis method using enhanced multi-kernel maximum mean discrepancy to effectively extract features from multiple source domains for accurate target domain fault diagnosis. Extensive experiments on two fault diagnosis datasets demonstrate the superior performance of the proposed method.