- Tytuł:
- Data-driven models for fault detection using kernel PCA: A water distribution system case study
- Autorzy:
-
Nowicki, A.
Grochowski, M.
Duzinkiewicz, K. - Powiązania:
- https://bibliotekanauki.pl/articles/331249.pdf
- Data publikacji:
- 2012
- Wydawca:
- Uniwersytet Zielonogórski. Oficyna Wydawnicza
- Tematy:
-
maszyna ucząca się
detekcja uszkodzeń
monitorowanie
wykrywanie wycieku
machine learning
kernel PCA
fault detection
monitoring
water leakage detection - Opis:
- 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.
- Źródło:
-
International Journal of Applied Mathematics and Computer Science; 2012, 22, 4; 939-949
1641-876X
2083-8492 - Pojawia się w:
- International Journal of Applied Mathematics and Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł