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An Integrated Fuzzy Fault Tree Model with Bayesian Network-Based Maintenance Optimization of Complex Equipment in Automotive Manufacturing

Author

Listed:
  • Hamzeh Soltanali

    (Department of Biosystems Engineering, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran)

  • Mehdi Khojastehpour

    (Department of Biosystems Engineering, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran)

  • José Torres Farinha

    (Centre for Mechanical Engineering, Materials and Processes (CEMMPRE), 3030-199 Coimbra, Portugal
    Coimbra Institute of Engineering, Polytechnic of Coimbra, 3030-199 Coimbra, Portugal)

  • José Edmundo de Almeida e Pais

    (EIGeS—Research Centre in Industrial Engineering, Management and Sustainability, Lusófona University, Campo Grande 376, 1749-024 Lisboa, Portugal
    CISE—Electromechatronic Systems Research Centre, University of Beira Interior, 6201-001 Covilhã, Portugal)

Abstract

Process integrity, insufficient data, and system complexity in the automotive manufacturing sector are the major uncertainty factors used to predict failure probability (FP), and which are very influential in achieving a reliable maintenance program. To deal with such uncertainties, this study proposes a fuzzy fault tree analysis (FFTA) approach as a proactive knowledge-based technique to estimate the FP towards a convenient maintenance plan in the automotive manufacturing industry. Furthermore, in order to enhance the accuracy of the FFTA model in predicting FP, the effective decision attributes, such as the experts’ trait impacts; scales variation; and assorted membership, and the defuzzification functions were investigated. Moreover, due to the undynamic relationship between the failures of complex systems in the current FFTA model, a Bayesian network (BN) theory was employed. The results of the FFTA model revealed that the changes in various decision attributes were not statistically significant for FP variation, while the BN model, that considered conditional rules to reflect the dynamic relationship between the failures, had a greater impact on predicting the FP. Additionally, the integrated FFTA–BN model was used in the optimization model to find the optimal maintenance intervals according to the estimated FP and total expected cost. As a case study, the proposed model was implemented in a fluid filling system in an automotive assembly line. The FPs of the entire system and its three critical subsystems, such as the filling headset, hydraulic–pneumatic circuit, and the electronic circuit, were estimated as 0.206, 0.057, 0.065, and 0.129, respectively. Moreover, the optimal maintenance interval for the whole filling system considering the total expected costs was determined as 7th with USD 3286 during 5000 h of the operation time.

Suggested Citation

  • Hamzeh Soltanali & Mehdi Khojastehpour & José Torres Farinha & José Edmundo de Almeida e Pais, 2021. "An Integrated Fuzzy Fault Tree Model with Bayesian Network-Based Maintenance Optimization of Complex Equipment in Automotive Manufacturing," Energies, MDPI, vol. 14(22), pages 1-21, November.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:22:p:7758-:d:682735
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    References listed on IDEAS

    as
    1. Hamzeh Soltanali & A.H.S Garmabaki & Adithya Thaduri & Aditya Parida & Uday Kumar & Abbas Rohani, 2019. "Sustainable production process: An application of reliability, availability, and maintainability methodologies in automotive manufacturing," Journal of Risk and Reliability, , vol. 233(4), pages 682-697, August.
    2. Mohammad Yazdi & Farzaneh Nikfar & Mahnaz Nasrabadi, 2017. "Failure probability analysis by employing fuzzy fault tree analysis," 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. 8(2), pages 1177-1193, November.
    3. Ajith Tom James & O. P. Gandhi & S. G. Deshmukh, 2018. "Fault diagnosis of automobile systems using fault tree based on digraph modeling," 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. 9(2), pages 494-508, April.
    4. Chemweno, Peter & Pintelon, Liliane & Muchiri, Peter Nganga & Van Horenbeek, Adriaan, 2018. "Risk assessment methodologies in maintenance decision making: A review of dependability modelling approaches," Reliability Engineering and System Safety, Elsevier, vol. 173(C), pages 64-77.
    5. Volkanovski, Andrija & ÄŒepin, Marko & Mavko, Borut, 2009. "Application of the fault tree analysis for assessment of power system reliability," Reliability Engineering and System Safety, Elsevier, vol. 94(6), pages 1116-1127.
    6. Leimeister, Mareike & Kolios, Athanasios, 2018. "A review of reliability-based methods for risk analysis and their application in the offshore wind industry," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 1065-1076.
    7. Maria Holgado & Marco Macchi & Stephen Evans, 2020. "Exploring the impacts and contributions of maintenance function for sustainable manufacturing," International Journal of Production Research, Taylor & Francis Journals, vol. 58(23), pages 7292-7310, December.
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    Cited by:

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