@misc{Nowicki_Adam_Data-driven,
 author={Nowicki, Adam and Grochowski, Michał and Duzinkiewicz, Kazimierz},
 howpublished={online},
 publisher={Zielona Góra: Uniwersytet Zielonogórski},
 language={eng},
 abstract={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).},
 abstract={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.},
 type={artykuł},
 title={Data-driven models for fault detection using kernel PCA: A water distribution system case study},
 keywords={machine learning, kernel PCA, fault detection, monitoring, water leakage detection},
}