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Milling Fault Detection Method Based on Fault Tree Analysis and Hierarchical Belief Rule Base

Xiaoyu Cheng1, Mingxian Long1, Wei He1,2,*, Hailong Zhu1

1 School of Computer Science and Information Engineering, Harbin Normal University, Harbin 150025, China
2 High-Tech Institute of Xi’an, Xi’an, Shanxi, 710025, China

* Corresponding Author: Wei He. Email: email

Computer Systems Science and Engineering 2023, 46(3), 2821-2844. https://doi.org/10.32604/csse.2023.037330

Abstract

Expert knowledge is the key to modeling milling fault detection systems based on the belief rule base. The construction of an initial expert knowledge base seriously affects the accuracy and interpretability of the milling fault detection model. However, due to the complexity of the milling system structure and the uncertainty of the milling failure index, it is often impossible to construct model expert knowledge effectively. Therefore, a milling system fault detection method based on fault tree analysis and hierarchical BRB (FTBRB) is proposed. Firstly, the proposed method uses a fault tree and hierarchical BRB modeling. Through fault tree analysis (FTA), the logical correspondence between FTA and BRB is sorted out. This can effectively embed the FTA mechanism into the BRB expert knowledge base. The hierarchical BRB model is used to solve the problem of excessive indexes and avoid combinatorial explosion. Secondly, evidence reasoning (ER) is used to ensure the transparency of the model reasoning process. Thirdly, the projection covariance matrix adaptation evolutionary strategies (P-CMA-ES) is used to optimize the model. Finally, this paper verifies the validity model and the method's feasibility techniques for milling data sets.

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APA Style
Cheng, X., Long, M., He, W., Zhu, H. (2023). Milling fault detection method based on fault tree analysis and hierarchical belief rule base. Computer Systems Science and Engineering, 46(3), 2821-2844. https://doi.org/10.32604/csse.2023.037330
Vancouver Style
Cheng X, Long M, He W, Zhu H. Milling fault detection method based on fault tree analysis and hierarchical belief rule base. Comput Syst Sci Eng. 2023;46(3):2821-2844 https://doi.org/10.32604/csse.2023.037330
IEEE Style
X. Cheng, M. Long, W. He, and H. Zhu "Milling Fault Detection Method Based on Fault Tree Analysis and Hierarchical Belief Rule Base," Comput. Syst. Sci. Eng., vol. 46, no. 3, pp. 2821-2844. 2023. https://doi.org/10.32604/csse.2023.037330



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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