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

0545784 - ÚTIA 2022 RIV GB eng J - Journal Article
Ahmed, U. - Carpitella, Silvia - Certa, A.
An integrated methodological approach for optimising complex systems subjected to predictive maintenance.
Reliability Engineering & System Safety. Roč. 216, č. 1 (2021), č. článku 108022. ISSN 0951-8320. E-ISSN 1879-0836
Institutional support: RVO:67985556
Keywords : Complex systems * DEMATEL * ELECTRE TRI * FMECA * Maintenance management * Predictive maintenance * Risk management
OECD category: Occupational health
Impact factor: 7.247, year: 2021
Method of publishing: Limited access
http://library.utia.cas.cz/separaty/2021/MTR/carpitella-0545784.pdf https://www.sciencedirect.com/science/article/pii/S0951832021005317

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.
Permanent Link: http://hdl.handle.net/11104/0322773