A Study of the Impact of Predictive Maintenance Parameters on the Improvment of System Monitoring

Rim Louhichi, Mohamed Sallak and Jacques Pelletan
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Rim Louhichi: CNRS, Heudiasyc (Heuristics and Diagnosis of Complex Systems), Univeristé de Technologie de Compiègne, CS 60 319, 60203 Compiègne, France
Mohamed Sallak: CNRS, Heudiasyc (Heuristics and Diagnosis of Complex Systems), Univeristé de Technologie de Compiègne, CS 60 319, 60203 Compiègne, France
Jacques Pelletan: Institut Louis Bachelier, Université Paris 8, 28 Place de la Bourse, 75002 Paris, France

Mathematics, 2022, vol. 10, issue 13, 1-24

Abstract: Predictive maintenance can be efficiently improved by studying the sensitivity of the maintenance decisions with respect to changes in the proposed model parameters (costs, duration of reparation, etc.). To address this issue, we first propose an original approach that includes both maintenance costs and maintenance risks in the same objective function to minimize. This approach uses the RUL as an indicator of the health state of the system and supposes that the system is under regular inspections and can only be replaced by a new system in case of serious deterioration or failure. Then, we present a process of human decision making under uncertainty based on several criteria. Finally, we study and analyze the influence of the model parameters and their implications on the obtained maintenance policies. The study will lead to some recommendations that can improve the predictive maintenance decisions and help experts better handle maintenance costs.

Keywords: predictive maintenance; sensitivity analysis; remaining useful life; cost optimization; human decision; mechanical bearing system (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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