Adversarial weighted multi-source domain adaptation for compound fault diagnosis

Abstract Diagnosing compound faults in rotating machinery remains a major challenge due to the scarcity and imbalance of labeled training data, especially under varying operating conditions. To address this issue, this paper proposes a multi-source adversarial domain adaptation (MSADA) framework that integrates data augmentation, dynamic domain alignment, and multi-label learning. First, a deep convolutional generative adversarial network is employed to generate high-fidelity compound fault signals from single-fault data, thereby alleviating data imbalance and enriching label space. Second, the MSADA framework utilizes multiple heterogeneous source domains (SDs) and incorporates an adversarial weighting mechanism to dynamically assess the relevance between each SD and the target domain. This allows the model to suppress irrelevant information and enhance the contribution of shared fault categories during domain adaptation. Finally, a multi-label classifier is designed to decompose compound fault diagnosis into sequential binary classification tasks, explicitly modeling label dependencies.Experiments conducted on two datasets involving vibration and current signals demonstrate that MSADA consistently outperforms existing methods.