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2012 | 22 | 4 | 939-949
Tytuł artykułu

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

Treść / Zawartość
Warianty tytułu
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.
Rocznik
Tom
22
Numer
4
Strony
939-949
Opis fizyczny
Daty
wydano
2012
otrzymano
2011-10-08
poprawiono
2012-04-11
Twórcy
autor
  • Faculty of Electrical and Control Engineering, Gdańsk University of Technology, Narutowicza 11/12, 80-233 Gdańsk, Poland
  • Faculty of Electrical and Control Engineering, Gdańsk University of Technology, Narutowicza 11/12, 80-233 Gdańsk, Poland
  • Faculty of Electrical and Control Engineering, Gdańsk University of Technology, Narutowicza 11/12, 80-233 Gdańsk, Poland
Bibliografia
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  • Jezior, K., Mazur, K., Borowa, A., Grochowski, M. and Brdys, M. A. (2007). Multiregional PCA for leakage detection and localisation in DWDS-Chojnice case study, in J. Korbicz, K. Patan and M. Kowal (Eds.), Fault Diagnosis and Fault Tolerant Control, Academic Publishing House EXIT, Warsaw, pp. 303-310.
  • Kulczycki, P. and Charytanowicz, M. (2010). A complete gradient clustering algorithm formed with kernel estimators, International Journal of Applied Mathematics and Computer Science 20(1): 123-134, DOI: 10.2478/v10006-010-0009-3.
  • Li, J., Li, X. and Tao, D. (2008). KPCA for semantic object extraction in images, Pattern Recognition 41(10): 3244-3250.
  • Lima, C.A. and Coelho, A.L. (2011). Kernel machines for epilepsy diagnosis via EEG signal classification: A comparative study, Artificial Intelligence in Medicine 53(2): 83-95.
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Bibliografia
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