Kernel PCA in application to leakage detection in drinking water distribution system

Authors:
Adam Nowicki;Michał Grochowski
Affiliations:
Faculty of Electrical and Control Engineering, Gdansk University of Technology, Gdansk, Poland;Faculty of Electrical and Control Engineering, Gdansk University of Technology, Gdansk, Poland
Venue:
ICCCI'11 Proceedings of the Third international conference on Computational collective intelligence: technologies and applications - Volume Part I
Year:
2011

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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.