Fault detection of wheel in wheel/rail system using kurtosis beamforming method

Long Chen, Yat Sze Choy, Tian Gang Wang, Yan Kei Chiang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

22 Citations (Scopus)


Fault detection systems are typically applied in the railway industry to examine the structural health status of the wheel/rail system. We herein propose a time-domain kurtosis beamforming technique using an array of microphones for the fault identification and localisation of the wheel/rail system under an environment with high background noise. As an acoustics-based noncontact diagnosis method, this technique overcomes the challenge of the contact between the sensors and examined structures, and it is more applicable for impulsive signals of broadband nature, such as impact noise generated from faults on the wheel surface. Moreover, the application of kurtosis enables the identification and localisation at low signal-to-noise ratio. Under such circumstance, the impulsive signals generated by faults were totally merged in rolling noise and background noise. Meanwhile, different types of faults on the wheels could be identified and localised by observing the kurtosis value on the beamforming sound map. The effectiveness of the proposed method to diagnose the type of wheel fault with low signal-to-noise ratio and moving source has been validated experimentally. This method may provide a useful tool for the routine maintenance of trains.

Original languageEnglish
Pages (from-to)495-509
Number of pages15
JournalStructural Health Monitoring
Issue number2
Publication statusPublished - 1 Mar 2020


  • Array signal processing
  • impulsive signal
  • kurtosis
  • time-domain beamforming
  • wheel–rail contact

ASJC Scopus subject areas

  • Biophysics
  • Mechanical Engineering


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