Heterogeneous Mixture-of-Experts Network for Bearing Fault Diagnosis Under Strong Noise

Severe noise in industrial environments often leads to distribution shifts, posing a critical challenge for deep learning-based bearing fault diagnosis. Models trained on clean data typically degrade under such conditions due to their inability to effectively disentangle noise from fault-related features. To address this issue, we propose a Physics-Inspired Heterogeneous Mixture-of-Experts (PI-HMoE) network for robust fault diagnosis. Specifically, a multi-scale shape embedding module is designed to align convolutional kernels with fault characteristics, capturing complementary temporal patterns at different receptive-field scales to facilitate the separation of fault-relevant and noise-dominated representations. Furthermore, a global linear temporal mixer captures long-range periodic dependencies with linear computational complexity. In addition, a heterogeneous expert module integrates diverse operators through cluster-based gating to enhance model robustness. Extensive experiments on two standard bearing benchmark datasets demonstrate the superior noise robustness of our approach. Under severe zero-decibel noise conditions, the proposed model achieves diagnostic accuracies of 94.40% and 85.00%, outperforming state-of-the-art baselines by over 15% and 31%, respectively.