Work

Hierarchical tree-structured belief rule base for fault diagnosis of complex electromechanical systems

Year: 2026

Type: article

Abstract: Fault diagnosis of complex electromechanical systems is critical for ensuring safe operation of industrial equipment. Belief Rule Base (BRB) demonstrates significant advantages in small-sample and uncertain environments by combining expert knowledge with data-driven modeling. However, it faces the challenge of combinatorial explosion in high-dimensional feature scenarios. The number of rules grows exponentially. This severely limits its engineering applications. To address this challenge, this paper proposes a Hierarchical Tree-Structured BRB (HTS-BRB) method. It breaks the constraint of full rule combinations through hierarchical decomposition strategy. The proposed method first employs mutual information-based quantification integrated with expert knowledge to rank feature importance and select critical feature subsets. Subsequently, it constructs a hierarchical tree based on feature ranking. It employs an evolutionary algorithm to generate a high-quality sub-BRB set. Finally, it introduces the MAKER framework to model correlation coefficients for non-independent evidence fusion. The effectiveness of the proposed method is validated through permanent magnet synchronous motor fault diagnosis experiments.

Source: Scientific Reports

DOI: 10.1038/s41598-026-45997-x

Authors: Manlin Chen, Tao Su, Chao Cheng, You Cao, Boge Wen

Institution: Changchun University of Technology

Language: English

Topic: Machine Fault Diagnosis Techniques

Subfield: Control and Systems Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $2,690

Funder: National Natural Science Foundation of China