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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月1日

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  • Manlin Chen,
  • Tao Su,
  • Chao Cheng,
  • You Cao,
  • Boge Wen

摘要

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.