Enhanced wavelet transform-integrated MWRC-ResNet: a novel framework for interpretable and noise-robust rolling bearing fault diagnosis

Abstract Research on efficient detection methods for rolling bearings is crucial for enhancing the reliability and safety of mechanical equipment. Statistics indicate that over 30% of failures in rotating machinery are attributed to rolling bearings. This paper proposes the wavelet retention transformation and integrates it seamlessly with a residual neural network, resulting in a novel signal processing-based residual neural network framework (MWRC-ResNet). This approach significantly improves the accuracy and interpretability of fault detection in high-noise environments. The proposed method was experimentally validated using both the Case Western Reserve University dataset and the HIT dataset, and the experimental results show that its accuracy and noise resistance are superior to traditional models and other wavelet-based models. This approach not only improves the accuracy of fault detection but also offers better interpretability, providing an effective solution for rolling bearing fault diagnosis.