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A Knowledge Graph-Based Failure Information Fusion Method for Enhancing Reliability in Sustainable Systems

Author

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  • Yangqianhui Zhang

    (State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China)

  • Huayong Yang

    (State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
    School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China)

  • Dong Han

    (State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
    School of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China)

Abstract

Failure Mode and Effects Analysis (FMEA) serves as a fundamental process in reliability analysis, providing critical insights into support system planning and equipment design optimization. However, traditional FMEA processes encounter several limitations, including restricted data availability, subjective expert assessments, and rigid structural requirements. The current evaluation approaches for expert opinions are constrained by small sample sizes, stringent requirements for structural consistency, and high demands for logical cohesion. To address these issues, this paper proposes a failure information fusion method utilizing a knowledge graph. By improving decision-making reliability and resource efficiency, the proposed method contributes to sustainable maintenance practices and operational sustainability. Furthermore, the method incorporates knowledge embedding technologies, facilitating reasoning through the transformation of graph structures into matrix representations. This process uncovers potential failure relationships and improves analytical depth. A case study involving an aircraft system is presented to demonstrate the method’s effectiveness and versatility, showcasing its potential to enhance reliability and support system planning.

Suggested Citation

  • Yangqianhui Zhang & Huayong Yang & Dong Han, 2024. "A Knowledge Graph-Based Failure Information Fusion Method for Enhancing Reliability in Sustainable Systems," Sustainability, MDPI, vol. 16(23), pages 1-17, December.
    • Handle: RePEc:gam:jsusta:v:16:y:2024:i:23:p:10651-:d:1537125
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    References listed on IDEAS

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    4. Hengjie Zhang & Yucheng Dong & Jing Xiao & Francisco Chiclana & Enrique Herrera-Viedma, 2020. "Personalized individual semantics-based approach for linguistic failure modes and effects analysis with incomplete preference information," IISE Transactions, Taylor & Francis Journals, vol. 52(11), pages 1275-1296, November.
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