Physics-informed probabilistic deep network with interpretable mechanism for trustworthy mechanical fault diagnosis

The application of data-driven models based on the neural network is pivotal to developing an intelligent fault diagnostic flowchart. However, the reliability and interpretability of these models for fault prediction have presented challenges in neural network-based diagnostic approach. Another challenge is data availability, which has also been a limiting factor in using artificial Intelligence for condition monitoring and fault assessment in industrial mechanical systems. Consequently, this study proposes a Physics Informed Probabilistic Deep Network (PIPDN) framework to overcome these challenges. The PIPDN comprises two main components: the physical labelling module, designed to enhance physical labels with mechanical failure information, and the main body of PIPDN, responsible for learning fault representative features and generating smart data guided by conditional and physical labels. Furthermore, a multi-scale PIPDN model is developed to integrate the proposed uncertainty quantification (UQ) with decision-fusion module for accurate interpretation and enhanced fault diagnosis. The applicability, effectiveness, and superiority of the proposed framework and approach are validated using an experimental bearing dataset. The results indicate that integrating physical labels significantly assists the PIPDN model in capturing more accurate fault characteristics. This increases the importance of latent space features for subsequent fault diagnosis and also enhances the diagnostic interpretability. Furthermore, the addition of UQ-based decision-making module improves the reliability of the MS-PIPDN model by reducing epistemic uncertainty in the predictions.