Neural Approaches to Blind Separation and Cumulant Analysis and Its Application to Diagnostics of Nuclear Power Plants

Alexei Ourmanov

The problem concerned is to explore the possibility of using artificial intelligence techniques, namely neural networks, and design the appropriate neural network-based algorithm to detect signals of interest from multi-channel data recordings. The problem finds application in diagnostic systems of nuclear power plant with liquid-metal fast breeder. The idea of a whole approach is to make an adaptive diagnostic system of acoustic monitoring of a steam generator unit. The system is based on neural network feature extraction and pattern recognition of multi-channel acoustic signals generated by a steam generator unit. In the background noise environment the diagnostic system must detect water leaks in sodium which may occur in the steam generator unit under monitoring.


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