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Risk Evaluation Method Based on Fault Propagation and Diffusion

Author

Listed:
  • Liming Mu

    (School of Mechatronic Engineering, Xi’an Technological University, Xi’an 710021, China)

  • Yingzhi Zhang

    (School of Mechanical and Aerospace Engineering, Jilin University, Changchun 130022, China)

  • Qiyan Zhang

    (School of Materials Science and Chemical Engineering, Xi’an Technological University, Xi’an 710021, China)

Abstract

The high reliability demand of the machining center emphasizes the accuracy of the fault risk evaluation. In the traditional fault risk evaluation research of the machining center, the influence of fault mode is mostly based on subjective recommendation or does not consider the propagation and diffusion of fault, which makes the risk evaluation results different from the real situation. Therefore, this paper presents a framework to evaluate the fault risk for machining center components. A certain type of machining center is considered as a case study. The fault mode frequency ratio of components is calculated by fault mode analysis. The fault rate calculation is conducted based on the Johnson method. Considering that different fault modes have different influences on fault propagation breadth and depth, the hypergraph theory is used to build a hypernetwork model. The propagation and diffusion influence degree are defined to describe the propagation and diffusion process of faults. Then, the comprehensive influence degree of fault mode is calculated. The risk evaluation is realized by considering the component fault rate, fault mode frequency ratio, and the comprehensive influence degree of fault mode. The method proposed in this paper can provide a reference for the formulation of risk strategies for the machining center.

Suggested Citation

  • Liming Mu & Yingzhi Zhang & Qiyan Zhang, 2023. "Risk Evaluation Method Based on Fault Propagation and Diffusion," Mathematics, MDPI, vol. 11(19), pages 1-16, September.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:19:p:4083-:d:1248320
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    References listed on IDEAS

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    1. Kwag, Shinyoung & Gupta, Abhinav & Dinh, Nam, 2018. "Probabilistic risk assessment based model validation method using Bayesian network," Reliability Engineering and System Safety, Elsevier, vol. 169(C), pages 380-393.
    2. Wang, Qianlin & Diao, Xiaoxu & Zhao, Yunfei & Chen, Feng & Yang, Guoan & Smidts, Carol, 2021. "An expert-based method for the risk analysis of functional failures in the fracturing system of unconventional natural gas," Energy, Elsevier, vol. 220(C).
    3. Heidar Mohammadi & Zohreh Fazli & Hiro Kaleh & Hamid Reza Azimi & Saber Moradi Hanifi & Nasrin Shafiee, 2021. "Risk Analysis and Reliability Assessment of Overhead Cranes Using Fault Tree Analysis Integrated with Markov Chain and Fuzzy Bayesian Networks," Mathematical Problems in Engineering, Hindawi, vol. 2021, pages 1-17, October.
    4. Chemweno, Peter & Pintelon, Liliane & Muchiri, Peter Nganga & Van Horenbeek, Adriaan, 2018. "Risk assessment methodologies in maintenance decision making: A review of dependability modelling approaches," Reliability Engineering and System Safety, Elsevier, vol. 173(C), pages 64-77.
    5. Yifan Chen & Genbao Zhang & Yan Ran, 2019. "Risk Analysis of Coupling Fault Propagation Based on Meta-Action for Computerized Numerical Control (CNC) Machine Tool," Complexity, Hindawi, vol. 2019, pages 1-11, July.
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