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RailTwin: a digital twin framework for railway

Par Conseil national de recherches du Canada

DOITrouver le DOI : https://doi.org/10.1109/CASE49997.2022.9926529
AuteurRechercher : ; Rechercher : ; Rechercher : 1; Rechercher :
Affiliation
  1. Conseil national de recherches du Canada. Technologies numériques
FormatTexte, Article
Conférence2022 IEEE 18th International Conference on Automation Science and Engineering (CASE), August 20-24, 2022, Mexico City, Mexico
Sujetdeep learning; automation; transfer learning; reinforcement learning; inspection; rail transportation; digital twins
Résumé
This study aims at providing a conceptualized framework for railway to realize the Digital Twin (DT) beyond traditional structural modeling or information systems. First, we deduce a generic formula that shows that DT estimates the future states and decides actions beforehand. Then, based on this formula, we design a generic framework called RailTwin. The framework combines the insight of current states, the foresight representing the prediction of the future states, and the oversight based on the current and future state to enable automation and actuation. The key enabler of this framework to obtain these states is Artificial Intelligence (AI) technologies, including Deep Learning, Transfer Learning, Reinforcement Learning, and Explainable AI. We present a use case for asset health inspection and monitoring through the proposed framework.
Date de publication
Maison d’éditionIEEE
Dans
  • 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE) (20 août 2022) : 1767–1772.
Langueanglais
Publications évaluées par des pairsOui
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Identificateur de l’enregistrement27daa394-e3ec-40d9-ad3e-b77901c9d55b
Enregistrement créé2022-11-01
Enregistrement modifié2022-11-01
Date de modification :