Wayside detection of faults in railway axle bearings using time spectral kurtosis analysis on high frequency acoustic emission signals

Arash Amini, Mani Entezami, Zheng Huang, Hamed Rowshandel, Mayorkinos Papaelias

Research output: Contribution to journalArticlepeer-review

12 Citations (Scopus)
383 Downloads (Pure)

Abstract

Typical railway wheelsets consist of the wheels, axle and axle bearings. Faults can develop on any of the aforementioned components, but the most common are related to wheel and axle bearing defects. The continuous increase in train operating speeds means that failure of an axle bearing can lead to serious derailments, causing loss of life and severe disruption in the operation of the network, damage to the track and loss of confidence in rail transport by the general public. The rail industry has focused on the improvement of maintenance and online condition monitoring of rolling stock to reduce the probability of failure as much as possible. Current wayside systems such as hot axle box detectors and acoustic arrays can fail to detect defective bearings. This paper discusses the results of wayside high-frequency Acoustic Emission (AE) measurements carried on freight wagons with artificially induced damage in axle bearings in Long Marston, UK. Time spectral kurtosis (TSK) is applied for the analysis of the AE data. From the results obtained it is evident that TSK is capable of distinguishing the axle bearing defects from the random noises produced by different sources such as the wheel-rail interaction and changes in train speed.
Original languageEnglish
JournalAdvances in Mechanical Engineering
Volume8
Issue number11
Early online date1 Nov 2016
DOIs
Publication statusE-pub ahead of print - 1 Nov 2016

Keywords

  • Acoustic emission
  • wayside condition monitoring
  • axle bearing
  • time spectral kurtosis

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