A novel knowledge distillation framework for bearing fault diagnosis under imbalanced samples

As critical components of a machine, bearings are fundamental to maintaining system reliability, which emphasizes the importance of intelligent fault diagnosis. In industrial applications, bearing failures are episodic. As a result, a large amount of samples collected is normal, and the extreme imbalance of samples poses a challenge for bearing fault diagnosis (BFD) in industrial applications. In addition, industrial applications impose higher requirements for model lightweight due to the limitations in computational capability of the device. To address these challenges, a novel knowledge distillation framework with improved activation function and adaptive feature selection strategy was proposed for imbalanced BFD. Firstly, an enhanced activation function was introduced to improve the model’s nonlinear feature extraction capabilities, thereby boosting its performance when handling imbalanced datasets. Secondly, an adaptive feature selection strategy was proposed to ensure the extraction of sufficient information, thereby enhancing the model’s generalization capability across diverse devices. Finally, a novel knowledge distillation framework was proposed to downsize the model, which makes the model lightweight to meet the requirements of industrial applications. The effectiveness of the proposed methodology was experimentally validated on two imbalanced bearing datasets. The experiments demonstrate that the proposed methodology outperforms existing models in diagnostic performance.