基于LSTM的核电传感器多特征融合多步状态预测

针对核电工况参数预测的问题，利用核电站传感检测系统采集的大量时间序列，提出了基于长短时记忆网络（LSTM）的多特征融合多步状态预测模型。以某核电厂实时参数系统采集到的SG1蒸汽压力传感数据为研究对象，首先针对数据缺失、采样时标不一致问题进行数据预处理，然后完成基于LSTM的多特征融合多步状态预测模型的结构设计与建模，最后将本文提出的预测模型与循环神经网络（RNN）、门控循环单元（GRU）、本文模型-全连接层1以及单变量LSTM等多步预测模型进行比较。实验结果表明，本文提出的预测模型的拟合性能和预测性能整体最优，同时也验证了基于LSTM模型的深度学习方法在核电站运行安全保障领域的适用性。 

Multi-Feature Fusion Multi-Step State Prediction of Nuclear Power Sensor Based on LSTM

Aiming at the problem in the prediction of nuclear power working condition parameter, this paper uses a large number of time series collected by the nuclear power plant sensor detection system to propose a multi-feature fusion multi-step state prediction model based on long short-term memory network (LSTM). This paper takes the SG1 steam pressure sensor data collected by the real-time parameter system of nuclear power plants as the research object. Firstly, the data is preprocessed for the problems of missing data and inconsistent sampling time scales, and then the structural design and modeling of the multi-feature fusion multi-step state prediction model is completed based on LSTM. Finally, the prediction model proposed in this paper is compared with the multi-step prediction models such as Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU), Model-S1 layer and univariate LSTM. Experimental results show that the fitting performance and prediction performance of the prediction model proposed in this paper are with overall optimization, and it also verifies the applicability of the deep learning method based on the LSTM model in the field of nuclear power plant operation safety assurance.