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Pré-Publication, Document De Travail Année : 2021

Hybrid Model Learning for System Health Monitoring

Résumé

Obtaining a relevant model for a complex system is striven for in the Health Management community, as it allows to precisely monitor the health state of the system. This article introduces a method to obtain a hybrid model under the Heterogeneous Petri Net formalism for a system, using only data. The method is comprised of two steps, the learning of the Discrete Event System (DES) structure of the system using a clustering algorithm, then the learning of the continuous dynamics contained in the system and its various functioning modes using two regression algorithms. The method is applied on an academic example.
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Dates et versions

hal-03282377 , version 1 (09-07-2021)
hal-03282377 , version 2 (25-03-2022)
hal-03282377 , version 3 (21-04-2022)

Identifiants

  • HAL Id : hal-03282377 , version 1

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Amaury Vignolles, Elodie Chanthery, Pauline Ribot. Hybrid Model Learning for System Health Monitoring. 2021. ⟨hal-03282377v1⟩
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