Data-driven models for fault detection using kernel PCA: A water distribution system case study
Adam Nowicki, Michał Grochowski, Kazimierz Duzinkiewicz (2012)
International Journal of Applied Mathematics and Computer Science
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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...