Data-driven models for fault detection using kernel PCA: A water distribution system case study

• Autorzy: Adam Nowicki , Michał Grochowski , Kazimierz Duzinkiewicz
• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

machine learning; kernel PCA; fault detection; monitoring; water leakage detection

• Wydawca: University of Zielona Gora Press
• Czasopismo: International Journal of Applied Mathematics and Computer Science
• Rocznik: 2012
• Tom: 22
• Numer: 4
• Strony: 939-949

Daty

wydano

2012

otrzymano

2011-10-08

poprawiono

2012-04-11

Twórcy

autor

Adam Nowicki

• Faculty of Electrical and Control Engineering, Gdańsk University of Technology, Narutowicza 11/12, 80-233 Gdańsk, Poland

autor

Michał Grochowski

• Faculty of Electrical and Control Engineering, Gdańsk University of Technology, Narutowicza 11/12, 80-233 Gdańsk, Poland

autor

Kazimierz Duzinkiewicz

• Faculty of Electrical and Control Engineering, Gdańsk University of Technology, Narutowicza 11/12, 80-233 Gdańsk, Poland

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Identyfikatory

DOI

10.2478/v10006-012-0070-1