Abstract
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.
Highlights
The rail industry plays an important role in a nation’s economy and development and directly affects the lifestyle of the residents
This paper presents the importance and criticality of rail track condition monitoring to safe rail operations
We give a brief overview of the historical development of deep learning and list common deep learning models
Summary
The rail industry plays an important role in a nation’s economy and development and directly affects the lifestyle of the residents. The studies applying deep learning methods to rail track condition monitoring are reviewed where summaries are made according to the trend over time, the region of study, the raw data type, the pre-processing data, the purpose. This review framework provides a balance between deep learning methods and their application to rail track condition monitoring. The authors intend to provide a useful guide to researchers and practitioners who are interested in applying deep learning methods to rail track condition monitoring tasks. A systematic search and review of the application publications proves the relevance of deep learning methods to rail track condition monitoring tasks and provides insights into how such research works are carried out and what potential further studies can be followed up. This paper focuses on reviewing research works of deep learning applications to rail track condition monitoring since 2013
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