A semi-supervised learning framework for bearing fault recognition under strong noise

Intelligent bearing fault diagnosis methods based on deep learning have achieved massive results. However, acquiring sufficient labeled samples in engineering is a time-consuming and laborious task, and the signals are often contaminated by strong noise. Therefore, two main strategies are integrated to address these challenges. First, to address insufficient labeled samples, an improved dynamic pseudo-label learning strategy is developed, improving model generalization by involving more unlabeled data in training. Then, to solve the problem of strong noise faced in engineering, a noise reduction learning strategy based on self-supervised learning is proposed, extracting invariant features under noise conditions by a new optimization objective. In addition, a novel dynamic channel pruning strategy is proposed, which prunes channels that play a smaller role in the model, reducing the size of the model and decreasing the model hardware requirements for engineering applications. Finally, the effectiveness and robustness of the proposed strategies are verified on the two high-speed train wheelset-bearing datasets.