A deep belief rule base-based fault diagnosis method for complex systems

Complex systems are prone to faults due to their intricate structures, potentially impacting system stability. Therefore, fault diagnosis has become crucial for maintaining stable operation. In the field of complex systems, the combinatorial explosion problem in belief rule base (BRB) has attracted significant attention. The interdependence among system components leads to numerous variables and the need for rules, heightening model complexity. Regarding the combinatorial explosion problem, an improved belief rule network structure called deep BRB (DBRB) is proposed. First, the extreme gradient boosting (XGBoost) feature selection method is employed to choose the relatively important feature subset. Next, driven by the importance of features, different levels of features are input into the model, forming a complete and progressive network structure. Finally, the model undergoes the reasoning and optimization process. The effectiveness of the model is confirmed with a bearing fault dataset. After a comprehensive evaluation of multiple indicators, this method demonstrates a consistent improvement in classification performance as the depth increased. Moreover, compared to the traditional BRB model, this method notably reduces the number of parameters, improving its efficiency of processing complex data. In short, this method effectively tackles combinatorial explosion while ensuring model performance. The selection and assignment of feature subsets enhance the logic and readability of the model. Through the network structure, various fault features are captured well. This fault diagnosis method, rooted in the DBRB, offers a novel perspective on diagnosing complex system faults.