Detection and evaluation of rolling stock wheelset defects using acoustic emission

Eleni Giannouli, Mayorkinos Papaelias, Arash Amini, Zheng Huang, Valter Jantara Junior, Spyridon Kerkyras, Panukorn Krusuansombat, Fausto Garcia Marquez, Patrick Vallely

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Abstract

Acoustic emission (AE) can be employed for the early fault detection of rolling stock wheelset components. Research has been carried out on the development of a remote condition monitoring (RCM) technology for monitoring online rolling stock wheelset defects. Railway axle bearings and wheels are critical components that can develop faults at any time when in service. AE is a reliable passive RCM technique that can be employed for the quantitative evaluation of the structural integrity of rolling stock wheelsets. The emphasis of this study is placed on the results obtained from experimental work performed under laboratory and field testing conditions. Several laboratory tests were carried out using different axle bearing defects. In addition, a customised online RCM system installed on the Chiltern Rail Line, adjacent to a hot box axle detector, was used for comparison purposes. Using this, AE signal analysis was carried out in order to detect potential rolling stock faults. Defect type evaluation and quantification can also be achieved, leading to effective diagnosis of the structural rolling stock integrity of rolling stock wheels and axle bearings.
Original languageEnglish
Pages (from-to)403-408
JournalInsight: Non-Destructive Testing and Condition Monitoring
Volume63
Issue number7
DOIs
Publication statusPublished - 1 Jul 2021

Keywords

  • Acoustic emission
  • Axle bearings
  • Remote condition monitoring
  • Signal processing
  • Structural integrity

ASJC Scopus subject areas

  • Mechanics of Materials
  • Mechanical Engineering
  • Metals and Alloys
  • Materials Chemistry

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