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. 2024 Jan 28;14(1):2334.
doi: 10.1038/s41598-024-52829-3.

A complex system health state assessment method with reference value optimization for interpretable BRB

Qingxi Zhang #  1 , Kangle Li #  2 , Guangling Zhang  3 , Hailong Zhu  1 , Wei He  1
Affiliations

A complex system health state assessment method with reference value optimization for interpretable BRB

Qingxi Zhang et al. Sci Rep. .

Abstract

Health condition assessment is the basis for formulating and optimizing maintenance strategies of complex systems, which is crucial for ensuring the safe and stable operation of these systems. In complex system health condition assessment, it is not only necessary for the model to handle various uncertainties to ensure the accuracy of assessment results, but also to have a transparent and reasonable assessment process and interpretable, traceable assessment results. belief rule base (BRB) has been widely used as an interpretable modeling method in health condition assessment. However, BRB-based models currently face two issues: (1) inaccuracies in expert-provided parameters that can affect the model's accuracy, and (2) after model optimization, interpretability may be reduced. Therefore, this paper proposes a new method for complex system health condition assessment called interpretable BRB with reference value optimization (I-BRB). Firstly, to address the issue of inaccurate reference values, a reference value optimization algorithm with interpretability constraints is designed, which optimizes the reference values without compromising expert knowledge. Secondly, the remaining parameters are optimized using the projection covariance matrix adaptation evolution strategy (P-CMA-ES) with interpretability constraints to improve the model's accuracy. Finally, a case study evaluating the bearing components of a flywheel system is conducted to validate the proposed method. Experimental results demonstrate that I-BRB achieves higher accuracy in health condition assessment.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Interpretability criterions of I-BRB.
Figure 2
Figure 2
Reasonable belief distribution vs. Unreasonable belief distribution.
Figure 3
Figure 3
Reference value optimization algorithm.
Figure 4
Figure 4
The modeling process of complex system health state assessment model based on I-BRB.
Figure 5
Figure 5
P-CMA-ES Algorithm with interpretability constraints.
Figure 6
Figure 6
Reference values for I-BRB.
Figure 7
Figure 7
Belief comparison.
Figure 8
Figure 8
I-BRB evaluation results and actual values.
See this image and copyright information in PMC

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