A bearing fault diagnosis framework based on Comba attention mechanism and physical-informed neural network

Abstract

Traditional bearing fault diagnosis deep learning models rely on sufficient annotated data and powerful computing hardware, exhibiting poor anti-noise performance and weak generalization under small sample and mixed noise industrial conditions. Moreover, purely data-driven models fail to incorporate bearing contact mechanical laws, leading to low physical interpretability. This work develops a CAMHP framework coupling Hertz contact-based physics-informed neural networks (HPINNs) and physical-guided Comba attention mechanism (CAM). The VMD-HHT-RFE-MI preprocessing pipeline is utilized to purify noisy vibration signals. The main innovations of CAMHP are:(1) Embedding Hertz contact equations into PINN training and makes key mechanical parameters learnable to guarantee physically consistent diagnosis results.(2) Designing a lightweight CAM with dual feedback and block parallelism, realizing O(n) low-complexity temporal feature extraction guided by real-time contact state parameters. Validated on CWRU and JNU bearing datasets, CAMHP reaches 99.89% and 99.73% average diagnosis accuracy respectively. In mixed noise and small sample test scenarios, the proposed method consistently surpasses comparative algorithms with fast inference speed, demonstrating its robustness and interpretability for real-world rotating machinery fault monitoring.