A Self-Supervised Representation Learner for Bearing Fault Diagnosis Based on Motor Current Signals

The accurate diagnosis of bearing faults is crucial for ensuring the safety and reliability of rotary mechanical systems. In practice, the data size of healthy bearing samples is significantly larger than that of faulty samples. As a result, numerous data-driven models encounter challenges in effectively implementing fault diagnosis. The majority of scholars approach this situation primarily from the perspective of class imbalance while overlooking the effective utilization of these extensive healthy samples. To effectively harness healthy samples that can be easily collected, this article proposes a new self-supervised learner wavelet packet masked transformer (WPMFormer) for fault diagnosis. In the generative self-supervised learning paradigm introduced in this article, the utilization of abundant healthy samples facilitates the extraction of robust signal representations and enables analysis of potential relationships among their sub-sequences. This leads to obtaining a pre-trained model that possesses initialization parameters with strong generalization capabilities. To maintain the original neighborhood structure and provide more details for the signal reconstruction process, we first perform the wavelet packet decomposition in the signal preprocessing stage, enabling the discretization of the original signal into multiple sub-sequences. Second, we employ a substantial proportion of sub-sequence masking. It results in a significant and meaningful self-supervised task. Third, we propose an innovative reconstruction loss function that prioritizes training on data pairs with higher significance and mitigates the potential dominance of larger-value data points during the restructuring. The effectiveness of the proposed model and other tricks are fully validated on the bearing dataset generated by the railway motors experiment rig and a public bearing dataset. The experimental results demonstrate that the WPMFormer allows for learning a high-capacity model that generalizes well and outperforms other state-of-the-art methods in downstream tasks.