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A health assessment method with attribute importance modeling for complex systems using belief rule base

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  • Lian, Zheng
  • Zhou, Zhi-Jie
  • Hu, Chang-Hua
  • Wang, Jie
  • Zhang, Chun-Chao
  • Zhang, Chao-Li

Abstract

In the health assessment for complex systems, system attributes refer to the indicators or components having an impact on the health state of the system. When assessing the health state of complex systems, the relative importance of system attributes should be accurately modeled, otherwise incorrect results may occur. Especially for some complex systems with small samples, data-driven methods are prone to overfitting. In this article, a new method using belief rule base (BRB) is proposed to model the relative importance of system attributes while assessing the health state. As an interpretable modeling tool, BRB constructs nonlinear mapping relationships between system attributes and health state. A data-knowledge hybrid-driven ensemble feature selection (DKH-EFS) method is developed to calculate the relative importance of the system attributes, namely attribute importance (AM). A random sensitivity analysis (RSA) method of the model output to the input of BRB is proposed to calculate the feature importance (FI) of BRB. A new parameter optimization model considering the consistency between the AM and FI is introduced to improve the modeling ability of BRB for the AM. A case study on the health assessment of the fiber optic gyroscope (FOG) validates the proposed method.

Suggested Citation

  • Lian, Zheng & Zhou, Zhi-Jie & Hu, Chang-Hua & Wang, Jie & Zhang, Chun-Chao & Zhang, Chao-Li, 2024. "A health assessment method with attribute importance modeling for complex systems using belief rule base," Reliability Engineering and System Safety, Elsevier, vol. 251(C).
    • Handle: RePEc:eee:reensy:v:251:y:2024:i:c:s0951832024004599
    DOI: 10.1016/j.ress.2024.110387
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