Browse > Article
http://dx.doi.org/10.11001/jksww.2019.33.2.159

Study on the applicability of the principal component analysis for detecting leaks in water pipe networks  

Kim, Kimin (Department of Civil and Environmental Engineering, Pusan National University)
Park, Suwan (Department of Civil and Environmental Engineering, Pusan National University)
Publication Information
Journal of Korean Society of Water and Wastewater / v.33, no.2, 2019 , pp. 159-167 More about this Journal
Abstract
In this paper the potential of the principal component analysis(PCA) technique for the application of detecting leaks in water pipe networks was evaluated. For this purpose the PCA was conducted to evaluate the relevance of the calculated outliers of a PCA model utilizing the recorded pipe flows and the recorded pipe leak incidents of a case study water distribution system. The PCA technique was enhanced by applying the computational algorithms developed in this study which were designed to extract a partial set of flow data from the original 24 hour flow data so that the effective outlier detection rate was maximized. The relevance of the calculated outliers of a PCA model and the recorded pipe leak incidents was analyzed. The developed algorithm may be applied in determining further leak detection field work for water distribution blocks that have more than 70% of the effective outlier detection rate. However, the analysis suggested that further development on the algorithm is needed to enhance the applicability of the PCA in detecting leaks by considering series of leak reports happening in a relatively short period.
Keywords
Principal component analysis; Water distribution blocks; Water pipe network; Leak; Computational algorithm; Flow data;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
연도 인용수 순위
1 Borges, L.A. and Ramirez, M.A. (2010). "Acoustic Water Leak Detection System", Water Distribution Systems Analysis Symposium, 1-3.
2 Covas, D., Ramos, H., Lopes, N. and Almeida, A.B. (2006). "Water pipe system diagnosis by transient pressure signals", Water Distribution Systems Analysis Symposium, ASCE, USA, 1-19.
3 Kapelan, Z. Savic, D.A. and Walters, G.A. (2004). Incorporation of prior information on parameters in inverse transient analysis for leak detection and roughness calibration, Urban Water J., 1(2), 129-143.   DOI
4 Mounce, S.R. Day, A.J. Wood, A.S. and Khan, A. (2002). A neural network approach to burst detection, Water Sci. Technol., 45(4-5), 237-246.   DOI
5 Muggleton, J.M. and Brennan, M.J. (2005). Axisymmetric wave propagation in buried, fluid-filled pipes: effects of wall discontinuities, J. Sound. Vib., 281(3-5), 849-867.   DOI
6 Muggleton, J.M. Brennan, M.J. Pinnington, R.J. and Gao, Y. (2006). A novel sensor for measuring the acoustic pressure in buried plastic water pipes, J. Sound. Vib., 295(3-5), 1085-1098.   DOI
7 Palau, C.V. Arregui, F. and Ferrer, A. (2004). Using multivariate principal component analysis of injected water flows to detect anomalous behaviors in a water supply system. a case study, Water Supply, 4(3), 169-181.   DOI
8 Park, S. Jeon, D. Jung, S. Kim, J.H. and Lee, D. (2013). Identifying an appropriate analysis duration for the principal component analysis of water pipe flow data, J. Korean Soc. Water Wastewater, 27(3), 351-361.   DOI
9 Park, S. Jung, S.Y. and Sahleh, V. (2015). Determining the time of least water use for the major water usage types in district metered areas, J. Korean Soc. Water Wastewater, 29(3), 415-425.   DOI
10 Pilcher, R. Hamilton, S. Chapman, H. Ristovski, B. and Strapely, S. (2007). "Leak location and repair guidance notes", International Water Association, Water Loss Task Forces: Specialist Group Efficient Operation and Management, Bucharest, Romania, 12-18.
11 Ye, G and Fenner, Ra. (2011). Kalman fifiltering of hydraulic measurements for burst detection in water distribution systems, J. Pipeline Syst. Eng. Pract., 2, 14-22.   DOI
12 Stathis, J.A. and Loganathan, G.V. (1999). "Analysis of pressure-dependent leakage in water distribution systems, Analysis of pressure-dependent leakage in water distribution systems", Preparing for the 21st Century, 29th Annual Water Resources Planning and Management Conference, Tempe, AZ, USA.
13 Tajima, M. and Mita, A. (2008). Automatic leakage detection for water supply systems using principal component analysis, Materials Forum, 33, 87-94.
14 Xia, L. and Guo-jin, L. (2010). "Leak detection of municipal water supply network based on the cluster-analysis and fuzzy pattern recognition", International Conference on E-Product, E-Service, and EEntertainment(ICEEE).