A Review on Rail Defect Detection Systems Based on Wireless Sensors

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

21 Scopus Citations
View graph of relations

Author(s)

  • Yuliang Zhao
  • Zhiqiang Liu
  • Dong Yi
  • Xiaodong Yu
  • Xiaopeng Sha
  • Lianjiang Li
  • Zhikun Zhan

Related Research Unit(s)

Detail(s)

Original languageEnglish
Article number6409
Journal / PublicationSensors
Volume22
Issue number17
Online published25 Aug 2022
Publication statusPublished - Sept 2022

Link(s)

Abstract

Small defects on the rails develop fast under the continuous load of passing trains, and this may lead to train derailment and other disasters. In recent years, many types of wireless sensor systems have been developed for rail defect detection. However, there has been a lack of comprehensive reviews on the working principles, functions, and trade-offs of these wireless sensor systems. Therefore, we provide in this paper a systematic review of recent studies on wireless sensor-based rail defect detection systems from three different perspectives: sensing principles, wireless networks, and power supply. We analyzed and compared six sensing methods to discuss their detection accuracy, detectable types of defects, and their detection efficiency. For wireless networks, we analyzed and compared their application scenarios, the advantages and disadvantages of different network topologies, and the capabilities of different transmission media. From the perspective of power supply, we analyzed and compared different power supply modules in terms of installation and energy harvesting methods, and the amount of energy they can supply. Finally, we offered three suggestions that may inspire the future development of wireless sensor-based rail defect detection systems.

Research Area(s)

  • rail defects detection, railway sensors, wireless sensing system

Citation Format(s)

A Review on Rail Defect Detection Systems Based on Wireless Sensors. / Zhao, Yuliang; Liu, Zhiqiang; Yi, Dong et al.
In: Sensors, Vol. 22, No. 17, 6409, 09.2022.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

Download Statistics

No data available