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

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 propo...

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Autores principales:	Hamzeh Soltanali, Mehdi Khojastehpour, José Torres Farinha, José Edmundo de Almeida e Pais
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	automotive industry Bayesian network fault tree analysis fuzzy set theory maintenance optimization uncertainty Technology T
Acceso en línea:	https://doaj.org/article/a166a252837a4dd69c73afc1de631605
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spelling	oai:doaj.org-article:a166a252837a4dd69c73afc1de6316052021-11-25T17:28:32ZAn Integrated Fuzzy Fault Tree Model with Bayesian Network-Based Maintenance Optimization of Complex Equipment in Automotive Manufacturing10.3390/en142277581996-1073https://doaj.org/article/a166a252837a4dd69c73afc1de6316052021-11-01T00:00:00Zhttps://www.mdpi.com/1996-1073/14/22/7758https://doaj.org/toc/1996-1073Process 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.Hamzeh SoltanaliMehdi KhojastehpourJosé Torres FarinhaJosé Edmundo de Almeida e PaisMDPI AGarticleautomotive industryBayesian networkfault tree analysisfuzzy set theorymaintenance optimizationuncertaintyTechnologyTENEnergies, Vol 14, Iss 7758, p 7758 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	automotive industry Bayesian network fault tree analysis fuzzy set theory maintenance optimization uncertainty Technology T
spellingShingle	automotive industry Bayesian network fault tree analysis fuzzy set theory maintenance optimization uncertainty Technology T Hamzeh Soltanali Mehdi Khojastehpour José Torres Farinha José Edmundo de Almeida e Pais An Integrated Fuzzy Fault Tree Model with Bayesian Network-Based Maintenance Optimization of Complex Equipment in Automotive Manufacturing
description	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.
format	article
author	Hamzeh Soltanali Mehdi Khojastehpour José Torres Farinha José Edmundo de Almeida e Pais
author_facet	Hamzeh Soltanali Mehdi Khojastehpour José Torres Farinha José Edmundo de Almeida e Pais
author_sort	Hamzeh Soltanali
title	An Integrated Fuzzy Fault Tree Model with Bayesian Network-Based Maintenance Optimization of Complex Equipment in Automotive Manufacturing
title_short	An Integrated Fuzzy Fault Tree Model with Bayesian Network-Based Maintenance Optimization of Complex Equipment in Automotive Manufacturing
title_full	An Integrated Fuzzy Fault Tree Model with Bayesian Network-Based Maintenance Optimization of Complex Equipment in Automotive Manufacturing
title_fullStr	An Integrated Fuzzy Fault Tree Model with Bayesian Network-Based Maintenance Optimization of Complex Equipment in Automotive Manufacturing
title_full_unstemmed	An Integrated Fuzzy Fault Tree Model with Bayesian Network-Based Maintenance Optimization of Complex Equipment in Automotive Manufacturing
title_sort	integrated fuzzy fault tree model with bayesian network-based maintenance optimization of complex equipment in automotive manufacturing
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/a166a252837a4dd69c73afc1de631605
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An Integrated Fuzzy Fault Tree Model with Bayesian Network-Based Maintenance Optimization of Complex Equipment in Automotive Manufacturing

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