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

Adam Nowicki; Michał Grochowski; Kazimierz Duzinkiewicz

International Journal of Applied Mathematics and Computer Science (2012)

  • Volume: 22, Issue: 4, page 939-949
  • ISSN: 1641-876X

Abstract

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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.

How to cite

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Adam Nowicki, Michał Grochowski, and Kazimierz Duzinkiewicz. "Data-driven models for fault detection using kernel PCA: A water distribution system case study." International Journal of Applied Mathematics and Computer Science 22.4 (2012): 939-949. <http://eudml.org/doc/244510>.

@article{AdamNowicki2012,
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.},
author = {Adam Nowicki, Michał Grochowski, Kazimierz Duzinkiewicz},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {machine learning; kernel PCA; fault detection; monitoring; water leakage detection},
language = {eng},
number = {4},
pages = {939-949},
title = {Data-driven models for fault detection using kernel PCA: A water distribution system case study},
url = {http://eudml.org/doc/244510},
volume = {22},
year = {2012},
}

TY - JOUR
AU - Adam Nowicki
AU - Michał Grochowski
AU - Kazimierz Duzinkiewicz
TI - Data-driven models for fault detection using kernel PCA: A water distribution system case study
JO - International Journal of Applied Mathematics and Computer Science
PY - 2012
VL - 22
IS - 4
SP - 939
EP - 949
AB - 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.
LA - eng
KW - machine learning; kernel PCA; fault detection; monitoring; water leakage detection
UR - http://eudml.org/doc/244510
ER -

References

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