This paper describes a classification method for automatic fault detection in nuclear power plant (NPP) data. The method takes as input time series associated to specific parameters and realizes signal classification by using a clustering algorithm based on possibilistic C-means (PCM). This approach is applied to time series recorded in a CANDU® power plant and is validated by comparison with results provided by a classification method based on principal component analysis (PCA).

Application of Possibilistic C-Means for fault detection in Nuclear Power Plant data

PERASSO, ANNALISA;CAMPI, CRISTINA;TORACI, CRISTIAN;PIANA, MICHELE;MASSONE , Anna Maria
2015-01-01

Abstract

This paper describes a classification method for automatic fault detection in nuclear power plant (NPP) data. The method takes as input time series associated to specific parameters and realizes signal classification by using a clustering algorithm based on possibilistic C-means (PCM). This approach is applied to time series recorded in a CANDU® power plant and is validated by comparison with results provided by a classification method based on principal component analysis (PCA).
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11567/840755
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