Published in

American Society of Mechanical Engineers, Journal of Engineering for Gas Turbines and Power, 6(137), p. 062901

DOI: 10.1115/1.4028809

Links

Tools

Export citation

Search in Google Scholar

Application of Possibilistic C-Means for Fault Detection in Nuclear Power Plant Data

Journal article published in 2014 by Annalisa Perasso, Cristina Campi ORCID, Cristian Toraci, Francesco Benvenuto, Michele Piana, Anna Maria Massone
This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

Full text: Unavailable

Green circle
Preprint: archiving allowed
Upload
Green circle
Postprint: archiving allowed
Upload
Red circle
Published version: archiving forbidden
Policy details
Data provided by SHERPA/RoMEO

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).