On the identifiability testing in blind source separation using resampling technique
ICA'06 Proceedings of the 6th international conference on Independent Component Analysis and Blind Signal Separation
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Abstract: In this article, the authors present results obtained on industrial data with source separation techniques in an instantaneous mix. They introduce three applications developed to perform the monitoring of Electricite de France civil works and power plants. The first application concerns the monitoring of nuclear power plants. Each internal component generates specific vibration modes and "neutron noise" which is a combination of all modes generated. The aim of this study is to separate such independent vibration modes. The second application concerns dams supervision: it consists in separating the various types of motion of a dam according to their physical origin. The third application concerns nondestructive testing on steam generators in nuclear power plants. The aim is to reduce the flattening noise. The classical methods operate only when a noise reference is available. They propose to use a multi-sensor approach with the blind separation methods (the noise reference is not necessary). Considering the specifications of the signals, they obtain better performance using a two-order statistical algorithm than a higher-order statistical algorithm.