Multi-level predictive maintenance for multi-component systems

Kim-Anh Nguyen 1 Phuc Do Van 2 Antoine Grall 1
2 CRAN-ISET
CRAN - Centre de Recherche en Automatique de Nancy
Abstract : In this paper, a novel predictive maintenance policy with multi-level decision-making is proposed for multi-component system with complex structure. The main idea is to propose a decision-making process considered on two levels: system level and component one. The goal of the decision rules at the system level is to address if preventive maintenance actions are needed regarding the predictive reliability of the system. At component level the decision rules aim at identifying optimally a group of several components to be prevntively maintained when preventive maintenance is trigged due to the system level decision. Selecting optimal components is based on a cost-based group improvement factor taking into account the predictive reliability of the components, the economic dependencies as well as the location of the components in the system. Moreover, a cost model is developed to find the optimal maintenance decision variables. A 14-component system is finally introduced to illustrate the use and the performance of the proposed predictive maintenance policy. Different sensitivity analysis are also investigated and discussed. Indeed, the proposed policy provides more flexibility in maintenance decision-making for complex structure systems, hence leading to significant profits in terms of maintenance cost when compared with existing policies.
Document type :
Journal articles
Complete list of metadatas

https://hal.archives-ouvertes.fr/hal-01183225
Contributor : Phuc Do Van <>
Submitted on : Thursday, August 6, 2015 - 5:20:47 PM
Last modification on : Monday, September 16, 2019 - 4:35:56 PM

Identifiers

Collections

Citation

Kim-Anh Nguyen, Phuc Do Van, Antoine Grall. Multi-level predictive maintenance for multi-component systems. Reliability Engineering and System Safety, Elsevier, 2015, 144, pp.83-94. ⟨10.1016/j.ress.2015.07.017⟩. ⟨hal-01183225⟩

Share

Metrics

Record views

192