Nowicki, Adam ; Grochowski, Michał ; Duzinkiewicz, Kazimierz
Cordón, Oskar - ed. ; Kazienko, Przemysław - ed.
Data-driven models for fault detection using kernel PCA: A water distribution system case study
Hybrid and Ensemble Methods in Machine Learning
Group publication title:
Subject and Keywords:
machine learning ; kernel PCA ; fault detection ; monitoring ; water leakage detection
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.
Zielona Góra: Uniwersytet Zielonogórski
AMCS, Volume 22, Number 4 (2012) ; click here to follow the link