Abstract
One of the least difficult, quickest, and most useful strategies for the characteristic selection of real applications such as leak detection systems is the selection method based on Kullback-Leibler divergence. Nevertheless, this technique has issues when the preparation dataset is not sufficiently expansive. This paper proposes an improvement on this method to overcome its limitations. The assessment results show that the proposed strategy is steadier, more dependable, and has higher precision than the original technique.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Frauendorfer, R., Liemberger, R.: The Issues and Challenges of Reducing Non-Revenue Water. Asian Development Bank (2010)
Liu, Z., Kleiner, Y.: State of the art review of inspection technologies for condition assessment of water pipes. Measurement 46, 1–15 (2013)
Sinha, D.N.: Acoustic sensor for pipeline monitoring. Gas Technology Management Division Strategic Center for Natural Gas and Oil National Energy Technology Laboratory, p. 2 (2005)
Gao, Y., Brennan, M., Joseph, P., Muggleton, J., Hunaidi, O.: A model of the correlation function of leak noise in buried plastic pipes. J. Sound Vibr. 277, 133–148 (2004)
Gao, Y., Brennan, M., Joseph, P.: A comparison of time delay estimators for the detection of leak noise signals in plastic water distribution pipes. J. Sound Vibr. 292, 552–570 (2006)
Brunner, A.J., Barbezat, M.: Acoustic emission monitoring of leaks in pipes for transport of liquid and gaseous media: a model experiment. In: Advanced Materials Research, pp. 351–356. Trans Tech Publications (2006)
Yang, J., Wen, Y., Li, P.: Leak location using blind system identification in water distribution pipelines. J. Sound Vibr. 310, 134–148 (2008)
Tang, X., Liu, Y., Zheng, L., Ma, C., Wang, H.: Leak detection of water pipeline using wavelet transform method. In: 2009 International Conference on Environmental Science and Information Application Technology, pp. 217–220. IEEE, (2009)
Khulief, Y., Khalifa, A., Mansour, R.B., Habib, M.: Acoustic detection of leaks in water pipelines using measurements inside pipe. J. Pipeline Syst. Eng. Pract. 3, 47–54 (2011)
Juliano, T.M., Meegoda, J.N., Watts, D.J.: Acoustic emission leak detection on a metal pipeline buried in sandy soil. J. Pipeline Syst. Eng. Pract. 4, 149–155 (2012)
Puust, R., Kapelan, Z., Savic, D., Koppel, T.: A review of methods for leakage management in pipe networks. Urban Water J. 7, 25–45 (2010)
Yazdekhasti, S., Piratla, K.R., Atamturktur, S., Khan, A.A.: Novel vibration-based technique for detecting water pipeline leakage. Struct. Infrastruct. Eng. 13, 731–742 (2017)
Gao, Y., Brennan, M.J., Joseph, P., Muggleton, J., Hunaidi, O.: On the selection of acoustic/vibration sensors for leak detection in plastic water pipes. J. Sound Vibr. 283, 927–941 (2005)
Anastasopoulos, A., Kourousis, D., Bollas, K.: Acoustic emission leak detection of liquid filled buried pipeline. J. Acoust. Emission 27, 27–39 (2009)
Lim, J.: Underground pipeline leak detection using acoustic emission and crest factor technique. In: Shen, G., Wu, Z., Zhang, J. (eds.) Advances in Acoustic Emission Technology. SPP, vol. 158, pp. 445–450. Springer, New York (2015). https://doi.org/10.1007/978-1-4939-1239-1_41
Hunaidi, O., Chu, W., Wang, A., Guan, W.: Detecting leaks in plastic pipes. J. Am. Water Works Assoc. 92, 82–94 (2000)
Martini, A., Troncossi, M., Rivola, A.: Leak detection in water-filled small-diameter polyethylene pipes by means of acoustic emission measurements. Appl. Sci. 7, 2 (2017)
Grosse, C.U., Ohtsu, M.: Acoustic Emission Testing. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-69972-9
Meng, L., Yuxing, L., Wuchang, W., Juntao, F.: Experimental study on leak detection and location for gas pipeline based on acoustic method. J. Loss Prev. Process Ind. 25, 90–102 (2012)
Kang, J., Park, Y.-J., Lee, J., Wang, S.-H., Eom, D.-S.: Novel leakage detection by ensemble CNN-SVM and graph-based localization in water distribution systems. IEEE Trans. Ind. Electron. 65, 4279–4289 (2018)
Li, S., Song, Y., Zhou, G.: Leak detection of water distribution pipeline subject to failure of socket joint based on acoustic emission and pattern recognition. Measurement 115, 39–44 (2018)
Lin, J.: Divergence measures based on the Shannon entropy. IEEE Trans. Inf. Theory 37, 145–151 (1991)
Hunaidi, O., Chu, W.T.: Acoustical characteristics of leak signals in plastic water distribution pipes. Appl. Acoust. 58, 235–254 (1999)
Moore, S.: A review of noise and vibration in fluid-filled pipe systems. Acoustics, Brisbane, Australia (2016)
Ribeiro, M.I.: Gaussian probability density functions: properties and error characterization. Institute for Systems and Robotics, Lisboa, Portugal (2004)
Santosa, B.: Multiclass classification with cross entropy-support vector machines. Procedia Comput. Sci. 72, 345–352 (2015)
Acknowledgement
This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry & Energy (MOTIE) of the Republic of Korea (No. 20172510102130, No. 20192510102510).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Luong, T.N.T., Kim, JM. (2019). Leakage Classification Based on Improved Kullback-Leibler Separation in Water Pipelines. In: Dang, T., Küng, J., Takizawa, M., Bui, S. (eds) Future Data and Security Engineering. FDSE 2019. Lecture Notes in Computer Science(), vol 11814. Springer, Cham. https://doi.org/10.1007/978-3-030-35653-8_5
Download citation
DOI: https://doi.org/10.1007/978-3-030-35653-8_5
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-35652-1
Online ISBN: 978-3-030-35653-8
eBook Packages: Computer ScienceComputer Science (R0)