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Development of a risk-based maintenance decision making approach for automotive production line

Author

Listed:
  • Hamzeh Soltanali

    (Ferdowsi University of Mashhad)

  • Abbas Rohani

    (Ferdowsi University of Mashhad)

  • Mohammad Hossein Abbaspour-Fard

    (Ferdowsi University of Mashhad)

  • Aditya Parida

    (Luleå University of Technology)

  • José Torres Farinha

    (ISEC - Coimbra Institute of Engineering)

Abstract

Automotive industries require effective and reliable maintenance strategies to ensure high levels of availability and safety. Risk-based maintenance approach is a useful tool for maintenance decision making with the aim of reducing the overall risk in operating activities. In this paper, a Failure Mode and Effect Analysis (FMEA) model as one of the risk assessment techniques is developed with subjective information derived from domain experts. To overcome the drawbacks of traditional FMEA for risk priority number (RPN) estimation, a linguistic fuzzy set theory, through effective decision attributes in complex automotive equipment is conducted. The main attributes of this approach include the effect of experts’ traits, scales variation, using various membership functions and defuzzification algorithms on reliable Fuzzy-RPN (FRPN) estimation. The result of the proposed model revealed that altering membership functions and defuzzification algorithms had no significant effect on the FRPN estimation, but their values are highly affected by the number of scales. The sensitivity analysis showed that experts’ traits have no sensible impact on experts’ opinion for FRPN estimation, while the detectability index has more impact on FRPN variation. The result of risk classification number showed that the maintenance decision making could be included for the failure modes with the highest RPN values as a priority, which it would be useful to achieve the high level of availability and safety.

Suggested Citation

  • Hamzeh Soltanali & Abbas Rohani & Mohammad Hossein Abbaspour-Fard & Aditya Parida & José Torres Farinha, 2020. "Development of a risk-based maintenance decision making approach for automotive production line," 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(1), pages 236-251, February.
  • Handle: RePEc:spr:ijsaem:v:11:y:2020:i:1:d:10.1007_s13198-019-00927-1
    DOI: 10.1007/s13198-019-00927-1
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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. Gianpaolo Di Bona & Antonio Forcina & Domenico Falcone, 2018. "Maintenance strategy design in a sintering plant based on a multicriteria approach," International Journal of Management and Decision Making, Inderscience Enterprises Ltd, vol. 17(1), pages 29-49.
    3. Hu-Chen Liu & Yi-Zeng Chen & Jian-Xin You & Hui Li, 2016. "Risk evaluation in failure mode and effects analysis using fuzzy digraph and matrix approach," Journal of Intelligent Manufacturing, Springer, vol. 27(4), pages 805-816, August.
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    Cited by:

    1. Leoni, Leonardo & De Carlo, Filippo & Abaei, Mohammad Mahdi & BahooToroody, Ahmad & Tucci, Mario, 2023. "Failure diagnosis of a compressor subjected to surge events: A data-driven framework," Reliability Engineering and System Safety, Elsevier, vol. 233(C).
    2. Leonardo Leoni & Farshad BahooToroody & Saeed Khalaj & Filippo De Carlo & Ahmad BahooToroody & Mohammad Mahdi Abaei, 2021. "Bayesian Estimation for Reliability Engineering: Addressing the Influence of Prior Choice," IJERPH, MDPI, vol. 18(7), pages 1-16, March.
    3. Li, Ying & Liu, Peide & Li, Gang, 2023. "An asymmetric cost consensus based failure mode and effect analysis method with personalized risk attitude information," Reliability Engineering and System Safety, Elsevier, vol. 235(C).

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