<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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.</p></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.

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

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

International Journal of Applied Mathematics and Computer Science

Kernel Principal Component Analysis (KPCA), an example of machine learning, can be considered a non-linear extension of the PCA method. While various...

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

Online veröffentlicht: 28. Dez. 2012

Seitenbereich: 939 - 949

DOI: https://doi.org/10.2478/v10006-012-0070-1

This content is open access.