Kernel PCA in Application to Leakage Detection in Drinking Water Distribution System

Adam Nowicki; Michał Grochowski

Kernel PCA in Application to Leakage Detection in Drinking Water Distribution System

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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Category:: Conference activity
Type:: materiały konferencyjne indeksowane w Web of Science
Published in:: LECTURE NOTES IN COMPUTER SCIENCE no. 6922, pages 497 - 506,
ISSN: 0302-9743
Title of issue:: 3rd International Conference on Computational Collective Intelligence. Technologies and Applications strony 497 - 506
Language:: English
Publication year:: 2011
Bibliographic description:: Nowicki A., Grochowski M..: Kernel PCA in Application to Leakage Detection in Drinking Water Distribution System, W: 3rd International Conference on Computational Collective Intelligence. Technologies and Applications, 2011, ,.
Verified by:: Gdańsk University of Technology