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<p>Complex systems often experience various faults during operation. These faults can seriously affect system stability. Therefore, building an accurate and efficient fault diagnosis model is highly important. The belief rule base (BRB) is an effective method for fault diagnosis that combines expert knowledge with data-driven approaches. It can effectively handle uncertain information and has been widely applied in this field. However, class imbalance is common in real engineering applications. There are many more normal samples than fault samples, which often reduces the diagnostic accuracy of the model. To address this issue, a hierarchical BRB model based on the One-vs-Rest (OvR) strategy, called OvR-HBRB, is proposed. The model employs the OvR strategy to build a BRB classifier for each fault category to improve diagnostic capability. A learnable belief conversion matrix is designed to transform the outputs of BRB classifiers into multi-class belief distributions required by evidence reasoning (ER) fusion. Model parameters are optimized in stages via the covariance matrix adaptation evolution strategy (CMA-ES) to ensure convergence and diagnostic accuracy. Finally, experiments on the CWRU bearing and gear fault datasets verify the model’s effectiveness. The results show higher diagnostic accuracy under class imbalance conditions.</p>

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A fault diagnosis method for complex systems based on hierarchical belief rule base with One-vs-Rest strategy

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月20日

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  • Jiaqi Wu,
  • Ning Li,
  • Yingmei Li,
  • Lin Lin,
  • Boyu Liu

摘要

Complex systems often experience various faults during operation. These faults can seriously affect system stability. Therefore, building an accurate and efficient fault diagnosis model is highly important. The belief rule base (BRB) is an effective method for fault diagnosis that combines expert knowledge with data-driven approaches. It can effectively handle uncertain information and has been widely applied in this field. However, class imbalance is common in real engineering applications. There are many more normal samples than fault samples, which often reduces the diagnostic accuracy of the model. To address this issue, a hierarchical BRB model based on the One-vs-Rest (OvR) strategy, called OvR-HBRB, is proposed. The model employs the OvR strategy to build a BRB classifier for each fault category to improve diagnostic capability. A learnable belief conversion matrix is designed to transform the outputs of BRB classifiers into multi-class belief distributions required by evidence reasoning (ER) fusion. Model parameters are optimized in stages via the covariance matrix adaptation evolution strategy (CMA-ES) to ensure convergence and diagnostic accuracy. Finally, experiments on the CWRU bearing and gear fault datasets verify the model’s effectiveness. The results show higher diagnostic accuracy under class imbalance conditions.