RailTwin: A Digital Twin Framework For Railway

Authors

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 IntelligenceFollow

Document Type

Conference Proceeding

Publication Title

18th IEEE International Conference on Automation Science and Engineering

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.

First Page

1767

Last Page

1772

DOI

10.1109/CASE49997.2022.9926529

Publication Date

10-28-2022

Keywords

Deep learning, Reinforcement learning

Comments

IR conditions: non-described

Recommended Citation

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.