The problem concerned is to explore the possibility of using artificial intelligence techniques, namely neural networks, and design the appropriate neural networkbased algorithm to detect signals of interest from multichannel data recordings. The problem finds application in diagnostic systems of nuclear power plant with liquidmetal fast breeder. The idea of a whole approach is to make an adaptive diagnostic system fo 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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