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An integrated methodological approach for optimising complex systems subjected to predictive maintenance

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  • Ahmed, Umair
  • Carpitella, Silvia
  • Certa, Antonella

Abstract

The present paper addresses the relevant topic of maintenance management, widely recognised as a fundamental issue involving complex engineering systems and leading companies towards the optimisation of their assets while pursuing cost efficiency. With this regard, our research aims to provide companies with a hybrid methodological approach based on Multi-Criteria Decision-Making (MCDM) capable to deal with the main failures potentially involving complex systems subjected to predictive maintenance. Such an approach is going to be integrated within the framework of traditional Failure Mode Effects and Criticality Analysis (FMECA), whose strengths and weaknesses are considered. In particular, the ELimination Et Choix Traduisant la REalité (ELECTRE) TRI is suggested to sort failure modes into risk priority classes while the Decision Making Trial and Evaluation Laboratory (DEMATEL) is proposed to highlight the most influencing failures within each risk class. The approach is applied to a real service system, whose critical components are monitored by sensors and subjected to predictive maintenance. Final results clearly demonstrate as highlighting the elements impacting the occurrence of other failures within specific risk classes is a significant driver towards the implementation of effective maintenance, maximising the whole level of performance of the analysed system over its lifecycle.

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  • Ahmed, Umair & Carpitella, Silvia & Certa, Antonella, 2021. "An integrated methodological approach for optimising complex systems subjected to predictive maintenance," Reliability Engineering and System Safety, Elsevier, vol. 216(C).
    • Handle: RePEc:eee:reensy:v:216:y:2021:i:c:s0951832021005317
    DOI: 10.1016/j.ress.2021.108022
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    Cited by:

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    2. Mocellin, Paolo & Pilenghi, Lisa, 2023. "Semi-quantitative approach to prioritize risk in industrial chemical plants aggregating safety, economics and ageing: A case study," Reliability Engineering and System Safety, Elsevier, vol. 237(C).
    3. Chen, Jiayu. & Yao, Boqing & Lu, Qinhua & Wang, Xuhang & Yu, Pingchao & Ge, Hongjuan, 2024. "A safety dynamic evaluation method for missile mission based on multi-layered safety control structure model," Reliability Engineering and System Safety, Elsevier, vol. 241(C).
    4. Lingyun, Guo & Markus, Niffenegger & Jing, Zhou, 2022. "A novel procedure to evaluate the performance of failure assessment models," Reliability Engineering and System Safety, Elsevier, vol. 226(C).
    5. do Carmo, Pedro R.X. & do Monte, João Victor L. & Filho, Assis T. de Oliveira & Freitas, Eduardo & Tigre, Matheus F.F.S.L. & Sadok, Djamel & Kelner, Judith, 2023. "A data-driven model for the optimization of energy consumption of an industrial production boiler in a fiber plant," Energy, Elsevier, vol. 284(C).
    6. Yousra El kihel & Ali El kihel & El Mahdi Bouyahrouzi, 2022. "Contribution of Maintenance 4.0 in Sustainable Development with an Industrial Case Study," Sustainability, MDPI, vol. 14(17), pages 1-26, September.
    7. Silvia Carpitella, 2024. "Overcoming Barriers to Digital Transformation towards Greener Supply Chains in Automotive Paint Shop Operations," Sustainability, MDPI, vol. 16(5), pages 1-20, February.

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