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
Contributed by the Nuclear Division of ASME for publication in the JOURNAL OF ENGINEERING FOR GAS TURBINES AND POWER. Manuscript received June 30, 2014; final manuscript received October 7, 2014; published online December 9, 2014. Assoc. Editor: Igor Pioro.
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Perasso, A., Campi, C., Toraci, C., Benvenuto, F., Piana, M., and Massone, A. M. (June 1, 2015). "Application of Possibilistic C-Means for Fault Detection in Nuclear Power Plant Data." ASME. J. Eng. Gas Turbines Power. June 2015; 137(6): 062901. https://doi.org/10.1115/1.4028809
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