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

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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 followed by evaluation of its performance. Simulations prove that the presented approach is both flexible and efficient.

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International Journal of Applied Mathematics and Computer Science

Journal of the University of Zielona Góra

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IMPACT FACTOR 2017: 1.694
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