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Leak detection in water-filled plastic pipes through the application of tuned wavelet transforms to Acoustic Emission signals

Ahadi, M ; Sharif University of Technology | 2010

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  1. Type of Document: Article
  2. DOI: 10.1016/j.apacoust.2010.02.006
  3. Publisher: 2010
  4. Abstract:
  5. A new method to detect leakage in a water-filled plastic pipe is proposed. In this method, a leakage signal-signature in time domain is first captured by monitoring the Short Time Fourier Transforms (STFT) of AE (Acoustic Emission) signals over a relatively long time-interval. The captured signal is then used to find a mother wavelet (tuned wavelet) for the best signal localization in time and frequency domains. The technique for AE signal detection using tuned wavelet is then described. Practical application of the method proposed herein is then presented using a water-filled plastic pipe as a case study. Signals generated from this experimental setup are collected to identify leakage signal-signatures from other interfering signals (background, pipe natural frequency, splash and environmental noise). The results of the experiment prove that using tuned wavelet, AE events can be detected and identified precisely in time. In addition, sources of signals due to leakage and their respective energy levels can also be recognized
  6. Keywords:
  7. Acoustic Emission ; Pipe leak detection ; Wavelet ; Acoustic emission signal ; AE signals ; Energy level ; Environmental noise ; Experimental setup ; Filled plastics ; Frequency domains ; Interfering signals ; Leakage signals ; Localization ; Mother wavelets ; Short time Fourier transforms ; Time domain ; Acoustic emission testing ; Acoustic emissions ; Leak detection ; Plastic pipe ; Plastics ; Signal processing ; Time domain analysis ; Wavelet transforms ; Signal detection
  8. Source: Applied Acoustics ; Volume 71, Issue 7 , 2010 , Pages 634-639 ; 0003682X (ISSN)
  9. URL: http://www.sciencedirect.com/science/article/pii/S0003682X10000423?np=y&npKey=1e20ebcd4c10ead6d127213b40dbf93669efb22e9273e17ab5ff4052badd97ab