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Selected Works of Abdulmotaleb El Saddik
Article
RailTwin: A Digital Twin Framework For Railway
18th IEEE International Conference on Automation Science and Engineering
  • Rahatara Ferdousi, The University of Ottawa, Ottawa, ON, Canada
  • Fedwa Laamarti, The University of Ottawa, Ottawa, ON, Canada & Mohamed bin Zayed University of Artificial Intelligence
  • Chunsheng Yang, National Research Council Canada, Ottawa, ON, Canada
  • Abdulmotaleb Elsaddik, Mohamed bin Zayed University of Artificial Intelligence
Link
Document Type
Conference Proceeding
Abstract

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. © 2022 IEEE.

DOI
10.1109/CASE49997.2022.9926529
Publication Date
10-28-2022
Keywords
  • Deep learning,
  • Reinforcement learning
Disciplines
Comments

IR conditions: non-described

Citation Information
R. Ferdousi, F. Laamarti, C. Yang and A. El Saddik, "RailTwin: A Digital Twin Framework For Railway," 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE), Mexico City, Mexico, 2022, pp. 1767-1772, doi: 10.1109/CASE49997.2022.9926529.