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A Failure Mode Assessment Model Based on Neutrosophic Logic for Switched-Mode Power Supply Risk Analysis

Author

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  • James J. H. Liou

    (Department of Industrial Engineering and Management, National Taipei University of Technology, 1, Sec. 3, Zhongxiao E. Rd., Taipei 10608, Taiwan)

  • Perry C. Y. Liu

    (Ph.D. Program at College of Management, National Taipei University of Technology, 1, Sec. 3, Zhongxiao E. Rd., Taipei 10608, Taiwan)

  • Huai-Wei Lo

    (Department of Industrial Engineering and Management, Chaoyang University of Technology, 168, Jifeng E. Rd., Taichung 413310, Taiwan)

Abstract

Reducing the potential risks in the manufacturing process to improve the reliability of the switched-mode power supply (SMPS) is a critical issue for the users’ safety. This paper proposes a novel failure mode and effects analysis (FMEA) model based on hybrid multiple criteria decision-making (MCDM), which adopts neutrosophic set theory into the proposed model. A developed neutrosophic Best Worst method (NBWM) is used to evaluate the weights of risk factors and determine their importance. Secondly, the neutrosophic Weight Aggregated Sum Product Assessments (NWASPAS) method is utilized to calculate the Risk Priority Number (RPN) of the failure modes. The proposed model improves the shortcomings of traditional FMEA and improves the practical applicability and effectiveness of the Best Worst method (BWM) and Weight Aggregated Sum Product Assessments (WASPAS) methods. In addition, this study uses neutrosophic logic to reflect the true judgments of experts in the assessment, which considers authenticity, deviation, and uncertainty to obtain more reliable information. Finally, an empirical case study from an SMPS company headquartered in Taiwan demonstrates the effectiveness and robustness of the proposed model. In addition, by comparing with two other FMEA models, the results show that the proposed model can more clearly reflect the true and effective risks in the assessment. The results can effectively help power supply manufacturers to assess risk factors and determine key failure modes.

Suggested Citation

  • James J. H. Liou & Perry C. Y. Liu & Huai-Wei Lo, 2020. "A Failure Mode Assessment Model Based on Neutrosophic Logic for Switched-Mode Power Supply Risk Analysis," Mathematics, MDPI, vol. 8(12), pages 1-19, December.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:12:p:2145-:d:454606
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    References listed on IDEAS

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    1. Gianpaolo Di Bona & Alessandro Silvestri & Antonio Forcina & Antonella Petrillo, 2018. "Total efficient risk priority number (TERPN): a new method for risk assessment," Journal of Risk Research, Taylor & Francis Journals, vol. 21(11), pages 1384-1408, November.
    2. Priyank Srivastava & Dinesh Khanduja & Subramaniam Ganesan, 2020. "Fuzzy methodology application for risk analysis of mechanical system in process industry," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 11(2), pages 297-312, April.
    3. Chemweno, Peter & Pintelon, Liliane & Van Horenbeek, Adriaan & Muchiri, Peter, 2015. "Development of a risk assessment selection methodology for asset maintenance decision making: An analytic network process (ANP) approach," International Journal of Production Economics, Elsevier, vol. 170(PB), pages 663-676.
    4. Rezaei, Jafar, 2016. "Best-worst multi-criteria decision-making method: Some properties and a linear model," Omega, Elsevier, vol. 64(C), pages 126-130.
    5. Rezaei, Jafar, 2015. "Best-worst multi-criteria decision-making method," Omega, Elsevier, vol. 53(C), pages 49-57.
    6. Dilbagh Panchal & Dinesh Kumar, 2017. "Risk analysis of compressor house unit in thermal power plant using integrated fuzzy FMEA and GRA approach," International Journal of Industrial and Systems Engineering, Inderscience Enterprises Ltd, vol. 25(2), pages 228-250.
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    Cited by:

    1. Sheng-Wei Lin & Huai-Wei Lo, 2024. "An FMEA model for risk assessment of university sustainability: using a combined ITARA with TOPSIS-AL approach based neutrosophic sets," Annals of Operations Research, Springer, vol. 342(3), pages 2119-2145, November.
    2. Antonio Jiménez-Martín, 2022. "Special Issue “Recent Advances and Applications in Multi Criteria Decision Analysis”," Mathematics, MDPI, vol. 10(13), pages 1-3, July.
    3. Ferenc Bognár & Csaba Hegedűs, 2022. "Analysis and Consequences on Some Aggregation Functions of PRISM (Partial Risk Map) Risk Assessment Method," Mathematics, MDPI, vol. 10(5), pages 1-19, February.

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