An Interpretable Bearing Fault Diagnosis Model Based on Hierarchical Belief Rule Base

Bearings are one of the main components of mechanical equipment and one of the primary components prone to faults.Therefore, conducting fault diagnosis on bearings is a key issue in mechanical equipment research.Belief rule base (BRB) is essentially an expert system that effectively integrates qualitative and quantitative information, demonstrating excellent performance in fault diagnosis.However, class imbalance often occurs in the diagnosis task, which poses challenges to the diagnosis.Models with interpretability can enhance decisionmakers' trust in the output results.However, the randomness in the optimization process can undermine interpretability, thereby reducing the level of trustworthiness in the results.Therefore, a hierarchical BRB model based on extreme gradient boosting (XGBoost) feature selection with interpretability (HFS-IBRB) is proposed in this paper.Utilizing a main BRB alongside multiple sub-BRBs allows for the conversion of a multi-classification challenge into several distinct binary classification tasks, thereby leading to enhanced accuracy.By incorporating interpretability constraints into the model, interpretability is effectively ensured.Finally, the case study of the actual dataset of bearing fault diagnosis demonstrates the ability of the HFS-IBRB model to perform accurate and interpretable diagnosis.