Object
Title: Data-driven models for fault detection using kernel PCA: A water distribution system case study
Contributor:
Cordón, Oskar - ed. ; Kazienko, Przemysław - ed.
Subtitle:
Hybrid and Ensemble Methods in Machine Learning
Group publication title:
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.
Publisher:
Zielona Góra: Uniwersytet Zielonogórski
Resource Identifier:
DOI:
Pages:
Source:
AMCS, Volume 22, Number 4 (2012) ; click here to follow the link
Language:
Rights:
Biblioteka Uniwersytetu Zielonogórskiego
Object collections:
- Digital Library of Zielona Góra > Repository > Faculties > Faculty of Computer, Electrical and Control Engineering
- Digital Library of Zielona Góra > Repository > Types of work > Articles
- Digital Library of Zielona Góra > Repository > Scientific journals and UZ publishing series > International Journal of Applied Mathematics and Computer Science (AMCS) > International Journal of Applied Mathematics and Computer Science (AMCS) (2012)
Last modified:
Oct 14, 2021
In our library since:
Sep 15, 2021
Number of object content hits:
166
All available object's versions:
https://www.zbc.uz.zgora.pl/publication/65820
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International Journal of Applied Mathematics and Computer Science (AMCS)
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| Edition name | Date |
|---|---|
| Data-driven models for fault detection using kernel PCA: A water distribution system case study | Oct 14, 2021 |
