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Rail track condition monitoring: a review on deep learning approaches
Author(s) -
Albert Ji,
Wai Lok Woo,
Eugene Wong,
Yang Thee Quek
Publication year - 2021
Language(s) - English
DOI - 10.20517/ir.2021.14
Subject(s) - track (disk drive) , deep learning , computer science , task (project management) , fast track , artificial intelligence , engineering , systems engineering , medicine , surgery , operating system
Rail track is a critical component of rail systems. Accidents or interruptions caused by rail track anomalies usually possess severe outcomes. Therefore, rail track condition monitoring is an important task. Over the past decade, deep learning techniques have been rapidly developed and deployed. In the paper, we review the existing literature on applying deep learning to rail track condition monitoring. Potential challenges and opportunities are discussed for the research community to decide on possible directions. Two application cases are presented to illustrate the implementation of deep learning to rail track condition monitoring in practice before we conclude the paper.

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