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

Yangqianhui Zhang (), Huayong Yang and Dong Han
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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
Dong Han: State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China

Sustainability, 2024, vol. 16, issue 23, 1-17

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.

Keywords: failure analysis; knowledge graph; sustainable maintenance; reliability engineering; link prediction; supportability planning (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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