Novel imbalanced subdomain adaption multiscale convolutional network for cross-domain unsupervised fault diagnosis of rolling bearings

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Data on the vibration signals collected from rolling bearings mostly belongs to health conditions, leading to an imbalanced data distribution. In addition, frequent switching of operating conditions results in unlabeled data collected under a specific working condition. This paper proposes a novel network for cross-domain unsupervised fault diagnosis of rolling bearings considering the imbalanced data to address these challenges. First, a multiscale parallel features extraction is developed, which can fully mine the rich high-level feature representation of various fault types from the original data and has a high value for fault identification. Second, a squeeze-and-excitation attention mechanism is constructed to enhance features conducive to model classification and suppress redundant features. Finally, a new loss function is proposed to optimize the model, which can accurately classify imbalanced source domain and easily align related subdomains of two domains. The proposed method was validated on multiple unsupervised cross-domain diagnostic tasks on two bearing datasets. Experimental results manifest that the proposed method has stable generalization performance and excellent robustness.