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).

Issue Section:
Nuclear Power
Keywords:
Intelligent control, Nonintrusive diagnostics, Nuclear
Topics:
Algorithms, Flaw detection, Nuclear power stations, Signals, Time series

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