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Data-driven models for fault detection using kernel pca:a water distribution system case study

Abstract

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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Category:
Articles
Type:
artykuł w czasopiśmie wyróżnionym w JCR
Published in:
International Journal of Applied Mathematics and Computer Science no. 22,
ISSN: 1641-876X
Language:
English
Publication year:
2012
Bibliographic description:
Nowicki A., Grochowski M., Duzinkiewicz K.: Data-driven models for fault detection using kernel pca:a water distribution system case study// International Journal of Applied Mathematics and Computer Science. -Vol. 22, nr. iss. 4 (2012),
Verified by:
Gdańsk University of Technology

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