Monitoring plays an important role in advanced control of complex dynamic systems. Precise information about system's behaviour, including faults detection, enables efficient control. Proposed method- Kernel Principal Component Analysis (KPCA), a representative of machine learning, skilfully takes full advantage of the well known PCA method and extends its application to nonlinear case. The paper explains the general idea of KPCA and provides an example of how to utilize it for fault detection problem. The efficiency of described method is presented for application of leakage detection in drinking water systems, representing a complex and distributed dynamic system of a large scale. Simulations for Chojnice town show promising results of detecting and even localising the leakages, using limited number of measuring points
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Informacje dodatkowe
- Kategoria
- Aktywność konferencyjna
- Typ
- materiały konferencyjne indeksowane w Web of Science
- Język
- angielski
- Rok wydania
- 2011
