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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:
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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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