哈尔滨商业大学学报（自然科学版）2026，Vol.42Issue(2)：171-178,214,9.
基于ConvLSTM的核电温度传感器数据重构研究
Data reconstruction of nuclear power plant cold leg temperature sensor based on ConvLSTM
张万洲 1刘永阔 1顾阳 1刘级 1石周鑫1
作者信息
- 1. 哈尔滨工程大学 核安全与仿真技术国防重点学科实验室,哈尔滨 150001
- 折叠
摘要
Abstract
The Convolutional Long Short-Term Memory(ConvLSTM)network effectively integrates the spatial feature extraction capability of convolutional layers with the temporal dependency modeling advantages of Long Short-Term Memory(LSTM)networks.Consequently,it has been increasingly utilized for predicting critical parameters of systems or equipment.Data reconstruction refers to the process of reconstructing faulty sensor data using available operational data.The application of the ConvLSTM model for data reconstruction of nuclear power plant process sensors enables operators to obtain timely and accurate operational status information of systems or equipment,thereby preventing misoperation caused by erroneous measurements from failed sensors and ensuring the proper functioning of systems or equipment.In this paper,the Fuqing nuclear power plant simulator dataset was utilized as experimental data,and a sensor fault model was developed to simulate the reconstruction process and results under actual sensor failure scenarios.Comparative analysis of CNN,LSTM,and CNN-LSTM models demonstrated that the ConvLSTM model exhibits superior reconstruction accuracy and applicability.关键词
核电站/传感器/卷积长短期记忆网络/数据重构/特征提取/数据修复Key words
nuclear power plant/sensor/convolutional long short-term memory network/data reconstruction/feature extraction/data recovery分类
信息技术与安全科学引用本文复制引用
张万洲,刘永阔,顾阳,刘级,石周鑫..基于ConvLSTM的核电温度传感器数据重构研究[J].哈尔滨商业大学学报（自然科学版）,2026,42(2):171-178,214,9.
