Rail damage detection method based on acoustic emission and wavelet singularity

Yang Song, Fan Wu, Dekou Liu, Xiaozhou Liu, Yiqing Ni

Research output: Journal article publicationJournal articleAcademic researchpeer-review

3 Citations (Scopus)


All right reserved. Many major derail accidents are closely related to the rail damage, so the study on the rail flaw detection technology is particularly important nowadays. The study is aiming at analyzing and comparing the characteristics of signals data before and after destruction, collected by a damage detection system. The damage and defect were judged by the differences between the processed data of destructive signals and nondestructive ones in time and frequency domain and according to the energy spectrum features. What's more, the locations of defects and damages were obtained by virtue of the singularities of destructive signals using three wavelet singularity analysis methods, including continuous wavelet transform, Mallat algorithm and à Trous algorithm. It is found that à Trous algorithm can give quite accurate information about real damage locations, which shows that this method can be used in the real damage detection for rails and provide us more precise defect location information.
Original languageEnglish
Pages (from-to)196-200
Number of pages5
JournalZhendong yu Chongji/Journal of Vibration and Shock
Issue number2
Publication statusPublished - 15 Jan 2017


  • Accoustic emission (AE)
  • Nondestructive test (NDT)
  • Rail
  • Singularity detection

ASJC Scopus subject areas

  • Mechanics of Materials
  • Acoustics and Ultrasonics
  • Mechanical Engineering


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