International Journal of applied mathematics and computer science

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Paper details

Number 4 - December 2012
Volume 22 - 2012

Data-driven models for fault detection using kernel PCA: A water distribution system case study

Adam Nowicki, Michał Grochowski, Kazimierz Duzinkiewicz

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.

machine learning, kernel PCA, fault detection, monitoring, water leakage detection