Your browser does not support JavaScript!
http://iet.metastore.ingenta.com
1887

access icon free Wavelet DT method for water leak-detection using a vibration sensor: an experimental analysis

  • Author(s): Miloud Bentoumi 1, 2 ; Djamel Chikouche 1, 2 ; Amar Mezache 2, 3 ; Haddi Bakhti 2, 4
    • View affiliations
    • Affiliations: 1: Laboratoire Analyse Signaux et Systèmes, University of Mohamed Boudiaf-M'sila, M'sila 28000, Algeria ;
      2: Department of Electronics, University of Mohamed Boudiaf-M'sila, M'sila 28000, Algeria ;
      3: Laboratoire Signaux et Systèmes de Communication, University of Frères Mentouri Constantine, Constantine 25010, Algeria ;
      4: Laboratoire Instrumentation Scientifique, Université Ferhat Abbas de Sétif 1, Sétif 19000, Algeria
  • Source: Volume 11, Issue 4, June 2017, p. 396 – 405
    DOI:  10.1049/iet-spr.2016.0113 , Print ISSN 1751-9675, Online ISSN 1751-9683
© The Institution of Engineering and Technology
Received 29/02/2016, Accepted 04/11/2016, Revised 03/10/2016, Published 17/11/2016

In this study, the authors propose and analyse a novel leak-detection method based on the ‘Haar’ continuous wavelet transform (CWT) and a double thresholding, i.e. CWTDT. Inspired by the idea of the binary integration technique in radar target detection, the algorithm analyses the non-stationary vibration signal issued from a water pipeline through which it decides whether or not there exists a leak in the water conveyance. To achieve this, the signal is first divided into several segments. Partial binary decisions within each segment are then obtained through the use of two preselected thresholds. The final binary decision is obtained by means of the ‘K out of L’ fusion rule. In doing this, the hardware leak system prototype is designed and a number of desirable leak positions in the water pipeline are first created to achieve the two best thresholds and ‘K out of L’ fusion rule. For comparison purposes, the performances of the proposed CWTDT method are assessed experimentally against those of the existing fast Fourier transform- and CWT-based methods under real operating conditions.

Inspec keywords: vibrations; sensors; wavelet transforms; leak detection; signal detection; Haar transforms; pipelines

Other keywords: wavelet DT method; vibration sensor; water pipeline; leak-detection method; water leak-detection; Haar continuous wavelet transform; double thresholding; partial binary decisions

Subjects: Signal detection; Integral transforms in numerical analysis; Integral transforms in numerical analysis; Sensing devices and transducers; Signal processing theory; Transducers and sensing devices

References

    1. 1)
      • 21. Santos, R.B., de Almeida, W.S., da Silva, F.V, et al: ‘Spectral analysis for detection of leaks in pipes carrying compressed air’, Chem. Eng. Trans., 2013, 32, pp. 1363–1368.
    2. 2)
      • 13. Ahadi, M., Bakhtiar, M.S.: ‘Leak detection in water filled plastic pipes through the application of tuned wavelet transforms to acoustic emission signals’, Appl. Acoust. J., 2010, 71, (7), pp. 634–639.
    3. 3)
      • 19. Addison, P.S.: ‘Wavelet transforms and the ECG: a review’, Physiol. Meas., 2005, 26, (5), pp. 155–199.
    4. 4)
      • 17. Chuanhu, G.E., Hongying, Y., Hao, Y., et al: ‘A fast leak locating method based on wavelet transform’, J. Tsinghua Sci. Technol., 2009, 14, (5), pp. 551–555.
    5. 5)
      • 5. Martini, A., Troncossi, M., Rivola, A.: ‘Automatic leak detection in buried plastic pipes of water supply networks by means of vibration measurements’, Shock Vib., 2015, 2015 (2015), pp. 1–13.
    6. 6)
      • 14. Taghvaei, M., Beck, S.B.M., Staszewski, W.J.: ‘Leak detection in pipelines using cepstrum analysis’, J. Meas. Sci. Technol., 2006, 17, (2), pp. 367–372.
    7. 7)
      • 23. Barkat, M., Varshney, P.K.: ‘Adaptive cell-averaging CFAR detection in distributed sensor networks’, IEEE Trans. Aerosp. Electron. Syst., 1991, 27, (3), pp. 424–429.
    8. 8)
      • 18. Mezache, A., Soltani, F.: ‘Threshold optimization of decentralized CFAR detection in Weibull clutter using genetic algorithms’, Signal Image Video Process., 2008, 2, (1), pp. 1–7.
    9. 9)
      • 8. Ekuakille, A.L., Vendramin, G., Trotta, A., et al: ‘STFT-based spectral analysis of urban waterworks leakage detection’. XIX Imeko World Congress Fundamental and Applied Metrology, Lisbon, Portugal, 6–11 September 2009, pp. 2172–2176.
    10. 10)
      • 1. Tang, X., Liu, Y., Zheng, L.: ‘Leak detection of water pipeline using wavelet transform and correlation method’, J. Environ. Technol. Eng., 2010, 3, (2), pp. 62–67.
    11. 11)
      • 25. Lee, B.Y., Tarng, Y.S.: ‘Application of the discrete wavelet transform to the monitoring of tool failure in end milling using the spindle motor current’, Int. J. Adv. Manuf. Technol., 1999, 15, (4), pp. 238–243.
    12. 12)
      • 3. Zhang, L., Wu, Y., Guo, L., et al: ‘Design and implementation of leak acoustic signal correlator for water pipelines’, Inf. Technol. J. , 2013, 12, (11), pp. 2195–2200.
    13. 13)
      • 20. Aggarwal, R., Singh, J.K., Gupta, V.K., et al: ‘Noise reduction of speech signal using wavelet transform with modified universal threshold’, Int. J. Comput. Appl., 2011, 20, (5), pp. 14–19.
    14. 14)
      • 26. Donoho, D.L.: ‘De-noising by soft thresholding’, IEEE Trans. Inf. Theory, 1995, 41, (3), pp. 613–627.
    15. 15)
      • 12. Hunaidi, O., Chu, W., Wang, A., et al: ‘Detecting leaks in plastic pipes’, J. Am. Water Works Assoc., 2000, 92, (2), pp. 82–94.
    16. 16)
      • 22. Brunner, A.J., Barbezat, M.: ‘Acoustic emission monitoring of leaks in pipes for transport of liquid and gaseous media: a model experiment’, Adv. Mater. Res., 2006, 13, (14), pp. 351–356.
    17. 17)
      • 11. Gao, Y., Brennan, M.J., Joseph, P.F.: ‘A comparison of time delay estimators for the detection of leak noise signals in plastic water distribution pipes’, J. Sound Vib., 2006, 292, (3–5), pp. 552–570.
    18. 18)
      • 15. Caron, D.: ‘Détectabilité des fuites par émission acoustique’, Tech. Ing., 2004, r2057, pp. 1–13.
    19. 19)
      • 9. Lay-Ekuakille, A., Trotta, A., Vendramin, G.: ‘FFT-based spectral response for smaller pipeline leak detection’. IEEE Int. Instrumentation and Measurement Technology Conf., Singapore, 5–7 May 2009, pp. 328–331.
    20. 20)
      • 16. Sarrate, R., Blesa, J., Nejjari, F., et al: ‘Sensor placement for leak detection and location in water distribution networks’. Seventh IWA Int. Conf. on Efficient Use and Management of Water (Efficient 2013), Paris, France, 22–25 October 2013, pp. 1–10.
    21. 21)
      • 24. Gini, F., Lombardini, F., Verrazzani, L.: ‘Decentralized CFAR detection with binary integration in Weibull clutter’, IEEE Trans. Aerosp. Electron. Syst., 1997, 33, (2), pp. 396–407.
    22. 22)
      • 6. Cataldo, A., Cannazza, G., Benedetto, E.D., et al: ‘A new method for detecting leaks in underground water pipelines’, IEEE Sens. J., 2012, 12, (6), pp. 1660–1667.
    23. 23)
      • 7. Yang, J., Yumei, W., Ping, L.: ‘Leak acoustic detection in water distribution pipelines’. Seventh World Congress on Intelligent Control and Automation, Chongqing, China, 25–27 June 2008, pp. 3057–3061.
    24. 24)
      • 4. Nasirian, A., Maghrebi, M.F., Yazdani, S.: ‘Leakage detection in water distribution network based on a new heuristic genetic algorithm model’, J. Water Resour. Prot., 2013, 5, (3), pp. 294–303.
    25. 25)
      • 10. Lay-Ekuakille, A., Vendramin, G., Trotta, A.: ‘Robust spectral leak detection of complex pipelines using filter diagonalization method’, IEEE Sens. J., 2009, 9, (11), pp. 1605–1614.
    26. 26)
      • 27. Aggarwal, R., Singh, J.K., Rathore, S., et al: ‘Noise reduction of speech signal using wavelet transform with modified universal threshold’, Int. J. Comput. Appl., 2011, 20, (5), pp. 0975–8887.
    27. 27)
      • 2. Claudia Deniss, E.A., Garza, C., Eduardo, L., et al: ‘Multi-leak detection with wavelet analysis in water distribution networks’. IEEE Mediterranean Conf. on Control & Automation, Barcelona, Spain, 3–6 July 2012, pp. 1155–1160.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-spr.2016.0113
Loading

Related content

content/journals/10.1049/iet-spr.2016.0113
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
Print this page
This is a required field
Please enter a valid email address