文章基本信息

标题：Data-driven models for fault detection using kernel PCA: A water distribution system case study
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作者：Adam Nowicki ; Michał Grochowski ; Kazimierz Duzinkiewicz 等
期刊名称：International Journal of Applied Mathematics and Computer Science
电子版ISSN：2083-8492
出版年度：2012
卷号：22
期号：4
DOI：10.2478/v10006-012-0070-1
出版社：De Gruyter Open
摘要：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.
关键词：machine learning; kernel PCA; fault detection; monitoring; water leakage detection