Research on Multi-parameter Synchronous Monitoring Technology of Nuclear Power Plant Equipment Status

The stable operation status and long-term operation data accumulation of nuclear power plant equipment have established a good data foundation for realizing data-driven intelligent monitoring of equipment status. In this paper, an intelligent monitoring method of equipment condition based on multi-parameter correlation is proposed, which includes three steps: modeling, training and inference, and a data-driven intelligent monitoring and early warning model of equipment condition is established. First, the system equipment monitoring parameters, parameter monitoring contents and correlation are identified and analyzed, and the correlation model of monitoring parameters is designed and established. Second, the historical data of normal operation of the equipment are collected and selected as training data, and the correlation model is trained based on BP feed forward neural network; Finally, the measured values of the monitoring parameters of the equipment are collected in real time, and the predicted values of each parameter are inferred based on the model, and the deviation between the measured value and the predicted value is monitored, and an early warning message is given when the deviation exceeds a predetermined threshold. This paper takes the heat exchanger and main feed pump of a power plant as an example to model and verify. The results show that the monitoring model proposed in this paper can effectively monitor the small and abnormal changes of equipment parameters synchronously, give early warning against early abnormalities, and maintain a very low false alarm rate.