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Article type: Research Article
Authors: Shi, Qingguo; * | Hu, Yihuai | Yan, Guohua
Affiliations: Merchant Marine College, Shanghai Maritime University, Shanghai, China
Correspondence: [*] Corresponding author. Qingguo Shi, Merchant Marine College, Shanghai Maritime University, Shanghai, China. E-mail: [email protected].
Abstract: The failure mode and effect analysis (FMEA) is an effective tool to analyze risks and potential effects of complex systems, and it is one of the most widely used risk analysis methods for complex systems as there often exists various factors that could affect the operation of the complex systems. Conventional FMEA methods have been limited to using crisp values to represent the assessments, which has been criticized for having many deficiencies. Marine diesel fuel injection system is an important part of marine diesel engine, and its failure could directly affect the performance of the marine diesel engine and even impact the safe operation of the ship. However, little attention has been paid to the FMEA of the marine diesel fuel injection system. To this end, this paper presents a novel FMEA method based on the best-worst method (BWM) and TOPSIS method with probabilistic linguistic term set (PLTS). Firstly, the PLTS is used to represent the uncertain and linguistic judgments of experts. Then, the BWM is extended with PLTS to determine the weights of different elements for FMEA, and the TOPSIS is extended with PLTS to assess and rank different failure modes. Finally, a case study on marine diesel fuel injection is presented, and the most critical failures are identified for improvement measures. The results show that the proposed method could help managers and engineerings identify the most important failure modes for marine diesel fuel injection system.
Keywords: Failure mode and effect analysis, risk analysis, probabilistic linguistic term set, marine diesel fuel injection system
DOI: 10.3233/JIFS-230870
Journal: Journal of Intelligent & Fuzzy Systems, vol. 45, no. 3, pp. 3835-3854, 2023
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