Publications Repository - Gdańsk University of Technology

Page settings

polski
Publications Repository
Gdańsk University of Technology

Treść strony

Data-driven models for fault detection using kernel pca:a water distribution system case study

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.

Authors

Additional information

Category
Publikacja w czasopiśmie
Type
artykuł w czasopiśmie wyróżnionym w JCR
Language
angielski
Publication year
2012

Source: MOSTWiedzy.pl - publication "Data-driven models for fault detection using kernel pca:a water distribution system case study" link open in new tab

Portal MOST Wiedzy link open in new tab