Elevating Interpretability in Bearing Fault Diagnosis: A Knowledge Distillation Framework Integrating Dynamic and Causal a Priori

This decade witnesses the privilege of deep learning in intelligent fault diagnosis. However, the limited interpretability poses significant challenges to comprehending and trusting the decision-making mechanisms. To tackle these issues, this paper proposes a knowledge distillation framework that synthesizes dynamic and causal a priori, aiming to capture the fault mechanisms while diminishing confounding side-effect embedded in data. Firstly, this study models rolling bearings using dynamic a priori knowledge, and the simulated data imbued with fault dynamics are then fed into the teacher model training pipeline to yield a deeper understanding of mechanical failures. Secondly, in cope with the probability drift and inconsistency in data collected from variable operating conditions, features undergo weighted fusion according to the causal a priori amongst variables to avoid “correlation trap” in fault classification. Finally, the knowledge distillation module enioys the above two steps adjusting parameters according to both priori simultaneously to achieve a better interpretability in fault diagnosis. In the experiment, the study demonstrates that the proposed framework enhances both the accuracy and interpretability of the model, achieving a remarkable accuracy rate of 95.8% under variable operating conditions.