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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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  • Duzinkiewicz, K., Borowa, A., Mazur, K., Grochowski, M., Brdys, M.A. and Jezior, K. (2008). Detection and localisation in drinking water distribution networks by multiregional PCA, Studies in Informatics and Control 17(2): 135-152.
  • Hoffman, H. (2007). Kernel PCA for novelty detection, Pattern Recognition 40(3): 863-874.
  • Hott, K. (2008). Robust face recognition under partial occlusion based on support vector machine with local Gaussian summation kernel, Image and Vision Computing 26(11): 1490-1498.
  • Isermann, R. (1984). Process fault detection based on modeling and estimation methods-A survey, Automatica 20(4): 387-404.
  • Jackson, J.E. (1991). A User's Guide to Principal Components, Wiley, Newark, NJ.
  • 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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