Data-driven models for fault detection using kernel PCA: A water distribution system case study
2012 ◽
Vol 22
(4)
◽
pp. 939-949
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Keyword(s):
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.
2011 ◽
2014 ◽
Vol 1030-1032
◽
pp. 1822-1827
2021 ◽
Vol 147
(3)
◽
pp. 04020111
2019 ◽
Vol 139
(12)
◽
pp. 757-766
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Keyword(s):
International Journal of Advanced Research in Electrical Electronics and Instrumentation Engineering
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2015 ◽
Vol 04
(05)
◽
pp. 3904-3910