Kernel Principal Component Analysis (KPCA), an example of machine learning, can be considered a non-linear extension of the PCA method. While various applications of KPCA are known, this paper explores the possibility to use it for building a data-driven model of a non-linear system-the water distribution system of the Chojnice town (Poland). This model is utilised for fault detection with the emphasis on water leakage detection. A systematic description of the system's framework is followed by evaluation of its performance. Simulations prove that the presented approach is both flexible and efficient.
@article{bwmeta1.element.bwnjournal-article-amcv22z4p939bwm,
author = {Adam Nowicki and Micha\l\ Grochowski and Kazimierz Duzinkiewicz},
title = {Data-driven models for fault detection using kernel PCA: A water distribution system case study},
journal = {International Journal of Applied Mathematics and Computer Science},
volume = {22},
year = {2012},
pages = {939-949},
zbl = {1283.93330},
language = {en},
url = {http://dml.mathdoc.fr/item/bwmeta1.element.bwnjournal-article-amcv22z4p939bwm}
}
Adam Nowicki; Michał Grochowski; Kazimierz Duzinkiewicz. Data-driven models for fault detection using kernel PCA: A water distribution system case study. International Journal of Applied Mathematics and Computer Science, Tome 22 (2012) pp. 939-949. http://gdmltest.u-ga.fr/item/bwmeta1.element.bwnjournal-article-amcv22z4p939bwm/
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