Data-driven models for fault detection using kernel PCA: A water distribution system case study - Nowicki, Adam; Grochowski, Michał; Duzinkiewicz, Kazimierz (Zielona Góra: Uniwersytet Zielonogórski)
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).
Keywords: machine learning; kernel PCA; fault detection; monitoring; water leakage detection