Intelligent Optical Fibre Sensing Networks Facilitate Shift to Predictive Maintenance in Railway Systems

Hwa Yaw Tam, Kang Kuen Lee, Shun Yee Liu, Lok Hin Cho, Kei Chun Cheng

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

3 Citations (Scopus)

Abstract

This paper depicts an optical fibre sensing network based railway health condition monitoring system that can facilitate predictive maintenance in railways. Machine learning is applied to develop learning models that can be used to detect and identify different types of track defects such as rail corrugations, dipped weld joints and rail crossings.

Original languageEnglish
Title of host publication2018 International Conference on Intelligent Rail Transportation, ICIRT 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538675281
DOIs
Publication statusPublished - 13 Feb 2019
Event2018 International Conference on Intelligent Rail Transportation, ICIRT 2018 - Singapore, Singapore
Duration: 12 Dec 201814 Dec 2018

Publication series

Name2018 International Conference on Intelligent Rail Transportation, ICIRT 2018

Conference

Conference2018 International Conference on Intelligent Rail Transportation, ICIRT 2018
Country/TerritorySingapore
CitySingapore
Period12/12/1814/12/18

Keywords

  • dipped weld joints
  • fibre Bragg grating
  • machine learning
  • optical fiber sensing network
  • predictive railway maintenance
  • rail corrugation

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Hardware and Architecture
  • Management Science and Operations Research
  • Automotive Engineering
  • Safety, Risk, Reliability and Quality
  • Control and Optimization
  • Transportation

Cite this