Application of FBG sensing technique for monitoring and early warning system of high-speed railway track conditions

Zhang, Yongjia; Liu, Fang; Jing, Yuhai; Li, Weilai

doi:10.1117/12.2265412

Cited by 9 publications

(6 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors of Zhang et al [ 294 ] developed a system consisting of a fiber Bragg sensor network (consisting of fiber-optic sensors , also known as optical fibers ) dedicated to track condition monitoring, especially for high-speed train systems. This condition-monitoring system (with an electric sensing device ) enables measurements of railway track temperature, displacement, and strain.…”

Section: Systematic Literature Reviewmentioning

confidence: 99%

See 1 more Smart Citation

Condition Monitoring of Rail Transport Systems: A Bibliometric Performance Analysis and Systematic Literature Review

Kostrzewski

Melnik

2021

Sensors

View full text Add to dashboard Cite

Condition monitoring of rail transport systems has become a phenomenon of global interest over the past half a century. The approaches to condition monitoring of various rail transport systems—especially in the context of rail vehicle subsystem and track subsystem monitoring—have been evolving, and have become equally significant and challenging. The evolution of the approaches applied to rail systems’ condition monitoring has followed manual maintenance, through methods connected to the application of sensors, up to the currently discussed methods and techniques focused on the mutual use of automation, data processing, and exchange. The aim of this paper is to provide an essential overview of the academic research on the condition monitoring of rail transport systems. This paper reviews existing literature in order to present an up-to-date, content-based analysis based on a coupled methodology consisting of bibliometric performance analysis and systematic literature review. This combination of literature review approaches allows the authors to focus on the identification of the most influential contributors to the advances in research in the analyzed area of interest, and the most influential and prominent researchers, journals, and papers. These findings have led the authors to specify research trends related to the analyzed area, and additionally identify future research agendas in the investigation from engineering perspectives.

show abstract

Section: Systematic Literature Reviewmentioning

confidence: 99%

“…A fiber-optic sensor, mentioned previously by Zhang et al (2017a) [ 294 ], is the next set of keywords worth consideration.…”

Section: Systematic Literature Reviewmentioning

confidence: 99%

Condition Monitoring of Rail Transport Systems: A Bibliometric Performance Analysis and Systematic Literature Review

Kostrzewski

Melnik

2021

Sensors

View full text Add to dashboard Cite

show abstract

“…To address this problem, Zhang et al [26] installed on a 1960 m long high-speed track section in Hong Kong an array of various FBG sensors. This includes 42 FBG temperature sensors, 117 FBG displacement sensors and 126 FBG stress and strain sensors.…”

Section: Fibre Bragg Gratingmentioning

confidence: 99%

Optical Fibres for Condition Monitoring of Railway Infrastructure—Encouraging Data Source or Errant Effort?

Vidović

Marschnig

2020

Applied Sciences

View full text Add to dashboard Cite

The condition of railway infrastructure is currently assessed by track recording cars, wayside equipment, onboard monitoring techniques and visual inspections. These data sources deliver valuable information for infrastructure managers on the asset’s condition but are mostly carried out in time-based intervals. This paper examines the potential of fibre optic cables, which are already installed in cable troughs alongside railway tracks, to monitor railway infrastructure conditions. The sensing technique, known as distributed acoustic/vibration sensing (DAS/DVS), relies on the effect of Rayleigh scattering and transforms the optical fibre into an array of “virtual microphones” in the thousands. This sensing method has the ability to be used over long distances and thus provide information about the events taking place in the proximity of the monitored asset in real-time. This study outlines the potential of DAS for the identification of different track conditions and isolated track defects. The results are linked to asset data of the infrastructure manager to identify the root cause of the detected signal anomalies and pattern. A methodology such as this allows for condition-based and component-specific maintenance planning and execution and avoids the installation of additional sensors. DAS can pave the way toward a permanent and holistic assessment of railway tracks.

show abstract

“…Kang et al [5] developed an FBG sensing system and graphical user interface (GUI) to monitor the wheel thickness changes in real time, which has been verified through the 1/6th sub-scale model test. Zhang et al [6] proposed a track temperature prediction system based on FBG sensors and relevance vector regression theory, developed a real-time online monitoring system for railway track temperature, displacement, and strain, and deployed it as a Guangzhou-Shenzhen-Hong Kong highspeed railway track state warning system. Buggy et al [7] monitored the condition of fishplates, stretcher poles, and switch blades using FBG strain sensors.…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Deep Learning in High-Speed Railway Track Detection Based on Distributed Fiber Acoustic Sensing

Wang

Liu

2022

Sensors

View full text Add to dashboard Cite

High deployment costs, safety risks, and time delays restrict traditional track detection methods in high-speed railways. Therefore, approaches based on optical sensors have become the most remarkable strategy in terms of deployment cost and real-time performance. Owing to the large amount of data obtained by sensors, it has been proven that deep learning, as a powerful data-driven approach, can perform effectively in the field of track detection. However, it is difficult and expensive to obtain labeled data from railways during operation. In this study, we used a segment of a high-speed railway track as the experimental object and deployed a distributed optical fiber acoustic system (DAS). We propose a track detection method that innovatively leverages semi-supervised deep learning based on image recognition, with a particular pre-processing for the dataset and a greedy algorithm for the selection of hyper-parameters. The superiority of the method was verified in both experiments and actual applications.

show abstract

Application of FBG sensing technique for monitoring and early warning system of high-speed railway track conditions

Cited by 9 publications

References 1 publication

Condition Monitoring of Rail Transport Systems: A Bibliometric Performance Analysis and Systematic Literature Review

Condition Monitoring of Rail Transport Systems: A Bibliometric Performance Analysis and Systematic Literature Review

Optical Fibres for Condition Monitoring of Railway Infrastructure—Encouraging Data Source or Errant Effort?

Semi-Supervised Deep Learning in High-Speed Railway Track Detection Based on Distributed Fiber Acoustic Sensing

Contact Info

Product

Resources

About