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. 2024 Dec 28;14(1):31365.
doi: 10.1038/s41598-024-82804-x.

An interpretable fault diagnosis method for aeroengine bearings based on belief rule based with a dynamic power set

Jinyuan Li  1 , Wei He  1 , Hailong Zhu  2
Affiliations

An interpretable fault diagnosis method for aeroengine bearings based on belief rule based with a dynamic power set

Jinyuan Li et al. Sci Rep. .

Abstract

Accurately identifying bearing faults in aeroengines is crucial for maintaining their lifespan and cost. However, most current models are black-box models, such as deep learning models such as deep neural networks. The decision-making process of these models is more complex and lacks interpretability, which results in insufficient credibility of the results. Furthermore, data collected in real industrial environments can suffer from unbalanced sample categories. Moreover, the models can suffer from local ignorance in the prediction process. These problems can lead to a decrease in the prediction accuracy of the model. Therefore, a fault diagnosis method based on the interpretable belief rule base with a dynamic power set (D-HBRBP-I) is proposed in this study. First, a diagnostic model based on a belief rule base with a dynamic power set was used to address the problem of sample category imbalance and local ignorance. Second, optimizing the model via the P-CMAES algorithm with interpretability constraints can ensure the interpretability of the model after optimization. Finally, experiments were conducted on an aeroengine-bearing dataset. The results show that the proposed model effectively solves the above problem while achieving 99% accuracy.

Keywords: Aeroengine bearings; Belief rule base; Fault diagnosis; Interpretability; Power set.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests. Ethics approval: This article does not contain any studies with human participants or animals performed by any of the authors. Informed consent: Informed consent was not required to participate, as no humans or animals were involved.

Figures

Fig. 1
Fig. 1
Belief degree in the example.
Fig. 2
Fig. 2
D-HBRBP-I diagnosis process.
Fig. 3
Fig. 3
Process for dynamically selecting power set reference values.
Fig. 4
Fig. 4
The optimization procedure of the P-CMAES.
Algorithm 1
Algorithm 1
.
Fig. 5
Fig. 5
Importance of feature for the MainBRB and SubBRBs.
Fig. 6
Fig. 6
Diagnostic results of D-HBRBP-I.
Fig. 7
Fig. 7
Indicators of comparative experiments.
Fig. 8
Fig. 8
Belief table of SubBRB0.
Fig. 9
Fig. 9
Belief Table of the SubBRB1.
See this image and copyright information in PMC

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