Abstract
Monitoring plays an important role in advanced control of complex dynamic systems. Precise information about system’s behaviour, including faults detection, enables efficient control. Proposed method- Kernel Principal Component Analysis (KPCA), a representative of machine learning, skilfully takes full advantage of the well known PCA method and extends its application to nonlinear case. The paper explains the general idea of KPCA and provides an example of how to utilize it for fault detection problem. The efficiency of described method is presented for application of leakage detection in drinking water systems, representing a complex and distributed dynamic system of a large scale. Simulations for Chojnice town show promising results of detecting and even localising the leakages, using limited number of measuring points.
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References
Thornton, J., Sturm, R., Kunkel, G.: Water Loss Control. The McGraw-Hill Companies, New York (2008)
Water Audits and Loss Control Programs - Manual of Water Supply Practices, M36. American Water Works Association (2009)
Jin, Y., Yumei, W., Ping, L.: Leak Acoustic Detection in Water Distribution Pipeline. In: The 7th World Congress on Intelligent Control and Automation, pp. 3057–3061. IEEE Press, New York (2008)
Xiao-Li, C., Chao-Yuan, J., Si-Yuan, G.: Leakage monitoring and locating method of water supply pipe network. In: The 7th International Conference on Machine Learning and Cybernetics, pp. 3549–3551. IEEE Press, New York (2008)
Mashford, J., Silva, D.D., Marney, D., Burn, S.: An approach to leak detection in pipe networks using analysis of monitored pressure values by support vector machine. In: 3rd International Conference on Network and System Security, pp. 534–539. IEEE Press, New York (2009)
Venkatasubramanian, V., Rengaswamy, R., Yin, K., Kavuri, S.: A review of process fault detection and diagnosis. Part I, II, III. Computers and Chemical Engineering 27, 293–346 (2003)
Duzinkiewicz, K., Borowa, A., Mazur, K., Grochowski, M., Brdys, M.A., Jezior, K.: Leakage Detection and Localization in Drinking Water Distribuition Networks by MultiRegional PCA. Studies in Informatics and Control 17(2), 135–152 (2008)
Jackson, J.E., Mudholkar, G.: Control procedures for residuals associated with principal component analysis. Technometrics 21, 341–349 (1979)
Schölkopf, B., Smola, A.J., Müller, K.R.: Nonlinear component analysis as a kernel eigenvalue problem. Neural Computation 10, 1299–1319 (1998)
Aizerman, M., Braverman, E., Rozonoer, L.: Theoretical foundations of the potential function method in pattern recognition learning. Automation and Remote Control 25, 821–837 (1964)
Hoffman, H.: Kernel PCA for novelty detection. Pattern Recognition 40, 863–874 (2007)
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Nowicki, A., Grochowski, M. (2011). Kernel PCA in Application to Leakage Detection in Drinking Water Distribution System. In: Jędrzejowicz, P., Nguyen, N.T., Hoang, K. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2011. Lecture Notes in Computer Science(), vol 6922. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23935-9_49
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DOI: https://doi.org/10.1007/978-3-642-23935-9_49
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23934-2
Online ISBN: 978-3-642-23935-9
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