CHERCHI, Tiffany; BAYSSE, Camille; DE SAPORTA, Benoîte; DUFOUR, François

doi:10.3850/981-973-0000-00-0_output

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CHERCHI, Tiffany
Institut Montpelliérain Alexander Grothendieck [IMAG]
Thales LAS France

BAYSSE, Camille
Thales LAS France

DE SAPORTA, Benoîte
Institut Montpelliérain Alexander Grothendieck [IMAG]

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Communication dans un congrès

Ce document a été publié dans

ESREL 2019 - 29th European Safety and Reliability Conference, 2019-09-22, Hanovre.

Résumé en anglais

We present an optimization problem for the maintenance of a multi-component system subject to random deteriorations or failures of its components, resulting in the evolution of the overall system's state. The system can be required to perform missions with deterministic occurrences and durations. Our long term objective is to set up an optimized maintenance policy to ensure the smooth running of missions while minimizing maintenance costs. The main idea of this work is to propose a mathematical model for the evolution of the system by using the formalism of a Markov Decision Processes (MDP). Through Monte Carlo simulations, we compare the performances of several reference policies.< Réduire

Mots clés en anglais

Maintenance Optimization

Multi-component system

Markov decision process

Monte Carlo simula- tions

Maintenance policy

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Optimal predictive maintenance policy for multi-component systems

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