Milling Fault Detection Method Based on Fault Tree Analysis and Hierarchical Belief Rule Base

Abstract

Expert knowledge is the key to modeling milling fault detection systems based on the belief rule base. The construction of an initial expert knowledge base seriously affects the accuracy and interpretability of the milling fault detection model. However, due to the complexity of the milling system structure and the uncertainty of the milling failure index, it is often impossible to construct model expert knowledge effectively. Therefore, a milling system fault detection method based on fault tree analysis and hierarchical BRB (FTBRB) is proposed. Firstly, the proposed method uses a fault tree and hierarchical BRB modeling. Through fault tree analysis (FTA), the logical correspondence between FTA and BRB is sorted out. This can effectively embed the FTA mechanism into the BRB expert knowledge base. The hierarchical BRB model is used to solve the problem of excessive indexes and avoid combinatorial explosion. Secondly, evidence reasoning (ER) is used to ensure the transparency of the model reasoning process. Thirdly, the projection covariance matrix adaptation evolutionary strategies (P-CMA-ES) is used to optimize the model. Finally, this paper verifies the validity model and the method's feasibility techniques for milling data sets.

A Case Study of Accident Analysis and Prevention for Coal Mining Transportation System Based on FTA-BN-PHA in the Context of Smart Mining Process

“…Based on FTA-AHP, Ren et al [24] proposed a collapse accident safety decision analysis method to qualitatively and quantitatively evaluate risk factors related to collapse accidents and determine the primary causes. To improve the accuracy and interpretability of the milling fault detection model, Cheng et al [25] introduced a milling fault detection method combined with FTA and hierarchical confidence rule base.…”