Online condition monitoring of rolling stock wheels and axle bearings

Mayorkinos Papaelias, Arash Amini, Zheng Huang, Patrick Vallely, Daniel Cardoso Dias, Spyridon Kerkyras

Research output: Contribution to journalArticlepeer-review

27 Citations (Scopus)
994 Downloads (Pure)

Abstract

The early detection of faults in rolling stock wheels and axle bearings is of paramount importance for rail infrastructure managers as it contributes to the safety of rail operations. In this paper we report on the key results that have arisen from the development and implementation of a novel condition monitoring system based on high-frequency acoustic emission and vibration analysis installed on a train. The novel system makes use of inexpensive and robust acoustic emission sensors and accelerometers, which can be easily installed on the axle bearing box with minimal intervention required. Experimental work carried out under actual conditions at the Long Marston rail track and on the Lisbon – Cas-Cais suburban line has proven that the developed system is capable of detecting wheel and axle bearing-related defects with various levels of severity.
Original languageEnglish
Pages (from-to)709-723
JournalProceedings of the Institution of Mechanical Engineers, Part F: Journal of Rail and Rapid Transit
Volume230
Issue number3
Early online date1 Dec 2014
DOIs
Publication statusPublished - 1 Mar 2016

Keywords

  • Railway
  • Train
  • Axle Bearing
  • onboard
  • fault detection
  • Acoustic emission
  • vibration
  • signal analysis

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