{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,10]],"date-time":"2026-03-10T11:56:22Z","timestamp":1773143782789,"version":"3.50.1"},"reference-count":81,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,19]],"date-time":"2021-11-19T00:00:00Z","timestamp":1637280000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003121","name":"Ferdowsi University of Mashhad","doi-asserted-by":"publisher","award":["FUM-52316"],"award-info":[{"award-number":["FUM-52316"]}],"id":[{"id":"10.13039\/501100003121","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Energies"],"abstract":"<jats:p>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\u2019 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\u2013BN 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\u2013pneumatic 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.<\/jats:p>","DOI":"10.3390\/en14227758","type":"journal-article","created":{"date-parts":[[2021,11,19]],"date-time":"2021-11-19T02:43:09Z","timestamp":1637289789000},"page":"7758","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["An Integrated Fuzzy Fault Tree Model with Bayesian Network-Based Maintenance Optimization of Complex Equipment in Automotive Manufacturing"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1972-1030","authenticated-orcid":false,"given":"Hamzeh","family":"Soltanali","sequence":"first","affiliation":[{"name":"Department of Biosystems Engineering, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8107-9026","authenticated-orcid":false,"given":"Mehdi","family":"Khojastehpour","sequence":"additional","affiliation":[{"name":"Department of Biosystems Engineering, Ferdowsi University of Mashhad, Mashhad 9177948974, Iran"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9694-8079","authenticated-orcid":false,"given":"Jos\u00e9 Torres","family":"Farinha","sequence":"additional","affiliation":[{"name":"Centre for Mechanical Engineering, Materials and Processes (CEMMPRE), 3030-199 Coimbra, Portugal"},{"name":"Coimbra Institute of Engineering, Polytechnic of Coimbra, 3030-199 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9508-6510","authenticated-orcid":false,"given":"Jos\u00e9 Edmundo de Almeida e","family":"Pais","sequence":"additional","affiliation":[{"name":"EIGeS\u2014Research Centre in Industrial Engineering, Management and Sustainability, Lus\u00f3fona University, Campo Grande 376, 1749-024 Lisboa, Portugal"},{"name":"CISE\u2014Electromechatronic Systems Research Centre, University of Beira Interior, 6201-001 Covilh\u00e3, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"114598","DOI":"10.1016\/j.eswa.2021.114598","article-title":"Predictive maintenance system for production lines in manufacturing: A machine learning approach using IoT data in real-time","volume":"173","author":"Ayvaz","year":"2021","journal-title":"Expert Syst. 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