A Physics-Informed Deep Encoding-Parsing Network for Cross-Domain Bearing Fault Diagnosis Under Noisy Sensor Data

Abstract Fault diagnosis based on bearing vibration signals is an important approach to improving the operational safety of complex equipment. However, existing zero-shot fault diagnosis methods across operating conditions typically require numerous learning samples and fail to model and analyze the physical mechanisms of bearing failure and the noisy sensor signal characteristics, limiting the domain adaptability of the diagnostic model. Therefore, this article proposes a zero-shot bearing fault diagnosis method based on physics-informed machine learning to improve the model’s cross-domain diagnostic performance under noisy sensor data. By integrating correlation analysis operations into neural networks, an automatic physical information learning module for bearing fault feature decoupling is established to achieve fault attribute separation guided by physical information. Then, leveraging the physical characteristics of periodic signal pulses in faulty bearings, a deep encoding-parsing network is designed to automatically generate fault attributes and analyze failure features for rolling bearing fault diagnosis. Comparisons with state-of-the-art methods demonstrate that the proposed method outperforms other methods in unknown domains, even under noisy sensor data. It also demonstrates great potential for cross-equipment implementation, potentially addressing the problem of data-free, cross-equipment adaptive fault diagnosis.