Hybrid Model Learning for System Health Monitoring - LAAS - Laboratoire d'Analyse et d'Architecture des Systèmes Access content directly
Conference Papers Year : 2022

Hybrid Model Learning for System Health Monitoring


Health monitoring approaches are usually either model-based or data-based. This article aims at using available data to learn a hybrid model to profit from both the data-based and model-based advantages. The hybrid model is represented under the Heterogeneous Petri Net formalism. The learning method is composed of two steps: the learning of the Discrete Event System (DES) structure using a clustering algorithm (DyClee) and the learning of the continuous system dynamics using two regression algorithms (Support Vector Regression or Random Forest Regression). The method is illustrated with an academic example.
Fichier principal
Vignette du fichier
SafeProcess2022_Hybrid_Model_Learning_for_System_Health_Monitoring.pdf (585.94 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

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


  • HAL Id : hal-03282377 , version 3


Amaury Vignolles, Elodie Chanthery, Pauline Ribot. Hybrid Model Learning for System Health Monitoring. Safeprocess 2022 : 11th IFAC Symposium on Fault Detection, Supervision and Safety for Technical Processes, Jun 2022, Paphos, Cyprus. ⟨hal-03282377v3⟩
42 View
7 Download


Gmail Facebook Twitter LinkedIn More