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Abstract
This paper aims to propose a Novel Condition-based maintenance (CBM) decision aid model for optimising the maintenance of complex multi-component systems. As the degradation level of each component is assumed to be independent and stochastic, it follows a specific probability distribution determined from historical data of experimental observations and inspection. The main objective is to optimise the total cost for providing maintenance actions and reducing the excess of spare parts usage. The decision support model consists of determining measurements on components with the aim of estimating the instant of time of removing predictively one or a group of components before they fail. The measurement model includes the mean residual lifetime (MRL) and some extensions developed for this purpose. For demonstrating the pertinency of the proposed model, we use a preventive maintenance strategy for one-component systems and a grouping/opportunistic maintenance for multi-component systems. Besides, a numerical comparative study performing these measurements is carried out using several examples and a case study from Electric energy distribution systems. The solution is illustrated as a decision-making optimal model for optimising the maintenance operations’ costs and the total number of spare parts. The numerical results and the comparison show the efficiency of the proposed approach.
Disclosure statement
No potential conflict of interest was reported by the author(s).
Data availability statement
Data sharing is not applicable to this article as no new data were created or used in this study.
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Funding
Notes on contributors
Rebaiaia Mohamed-Larbi
Mohamed-Larbi Rebaiaia holds a doctorate degree (PhD) in industrial engineering from Laval University (Canada), a doctorate degree in computer science from Batna University and a master degree by Science in Operational Research from Annaba University (Algeria). He is currently a researcher in production and operations management, and reliability and maintenance engineering, at the Science and Engineering faculty (Laval University), since 2008. Before 2007, he was an assistant professor in Computer Science and Operational Research at Annaba and Batna universities. His research interests include networks reliability evaluation and optimisation, maintenance engineering, production management and planning, and software engineering, machines learning and AI.
Ait-Kadi Daoud
Daoud Ait-Kadi holds a doctorate degree (PhD) in industrial engineering and a master’s degree in applied sciences from Polytechnique school at Montreal University (Canada). He is a full professor and researcher in the fields of modelling, optimisation and validation of the reliability, maintainability and availability of systems subject to one or more degradation modes, integrated logistical support, design and management of value creation networks, management of end-of-life products and reverse logistics, management of spare parts, optimisation of systems’ performance with a view to sustainable development.