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Communication Dans Un Congrès Année : 2022

Validating Condition-Based Maintenance Algorithms through Simulation

Marcel Chevalier
  • Fonction : Auteur
  • PersonId : 1107574
Léo Dupont
  • Fonction : Auteur
  • PersonId : 1187555
Sylvain Marié
Frédérique Roffet
  • Fonction : Auteur
  • PersonId : 1187556
Elena Stolyarova
William Templier
  • Fonction : Auteur
  • PersonId : 1187557
Costin Vasile

Résumé

Industrial end users are currently facing an increasing need to reduce the risk of unexpected failures and optimize their maintenance. This calls for both short-term analysis and long-term ageing anticipation. At Schneider Electric, we tackle those two issues using both Machine Learning and First Principles models. Machine learning models are incrementally trained from normal data to predict expected values and detect statistically significant short-term deviations. Ageing models are constructed from breaking down physical systems into sub-assemblies, then determining relevant degradation modes and associating each one to the right kinetic law. Validating such anomaly detection and maintenance models is challenging, both because actual incident and ageing data is rare and distorted by human interventions, and incremental learning depends on human feedback. To overcome these difficulties, we propose to simulate physics, systems and humansincluding asset maintenance operationsin order to validate the overall approaches in accelerated time and possibly choose between algorithmic alternatives.
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Dates et versions

hal-03854307 , version 1 (15-11-2022)

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  • HAL Id : hal-03854307 , version 1

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Marcel Chevalier, Léo Dupont, Sylvain Marié, Frédérique Roffet, Elena Stolyarova, et al.. Validating Condition-Based Maintenance Algorithms through Simulation. ICCIE 2022 : International Conference on Computers and Industrial Engineering, Jul 2022, Stockholm, Sweden. ⟨hal-03854307⟩

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