基于时间卷积胶囊网络的核动力装置事故诊断技术研究  

Research on Accident Diagnosis Technology of Nuclear Power Plant Based on Time Convolution Capsule Network

作　　者：邓强 王航 彭敏俊[1,2,3] 王晓昆 徐仁义 DENG Qiang;WANG Hang;PENG Minjun;WANG Xiaokun;XU Renyi(College of Nuclear Science and Technology,Harbin Engineering University,Harbin 150001,China;Nuclear Safety and Advanced Nuclear Technology Laboratory,Harbin Engineering University,Harbin 150001,China;Fundamental Science on Nuclear Safety and Simulation Technology Laboratory,Harbin Engineering University,Harbin 150001,China)

机构地区：[1]哈尔滨工程大学核科学与技术学院,黑龙江哈尔滨150001 [2]哈尔滨工程大学核安全与先进核能技术实验室,黑龙江哈尔滨150001 [3]哈尔滨工程大学核安全与仿真技术国防重点学科实验室,黑龙江哈尔滨150001

出　　处：《原子能科学技术》2023年第2期302-312,共11页Atomic Energy Science and Technology

基　　金：中国核工业集团青年科技人才启明星项目(KY90200210007)。

摘　　要：核动力装置是复杂的系统工程且具有潜在的放射性危险,因此要及时发现并处理其事故问题,避免造成严重后果。为建立核动力装置事故诊断方法,对操纵员的安全分析提供新的辅助手段,提出了一种基于时间卷积胶囊网络的核动力装置事故诊断方法。首先通过时间卷积核提取数据的时序信息,并降低计算难度,然后通过胶囊网络挖掘数据的深度向量特征,能够最大化利用运行信息中的数据特征,在事故数据较少的情况下也能得到很好的收敛效果,最后通过核动力装置全范围仿真模拟机获取事故仿真数据对模型进行训练和测试。研究结果表明,在核动力模拟机事故数据较少的情况下,相比于传统的深度学习方法,时间卷积胶囊网络模型有更好的收敛效果和诊断准确性。The nuclear power plant is a complex technical facility with potential radioactive hazards. Therefore, timely identification and treatment of accidents are necessary to avoid serious consequences. In order to quickly obtain information on nuclear power plant accidents and provide a new auxiliary means for the analysis of operator safety, it is necessary to study the accident diagnosis method and establish an accurate and reliable accident diagnosis model suitable for nuclear power plants. However, it is tough to establish a complete and accurate model of the nuclear power plant based on the model-based diagnosis method. The method based on expert knowledge needs a lot of expert experience knowledge which is difficult to obtain. Based on continuous data accumulated, the data-driven method can adaptively map the nonlinear relationship between symptoms and accidents, which is more suitable for fault diagnosis of the nuclear power plants. The data obtained by online monitoring for nuclear power plants are time series data with high data dimensions. However, there are few accident data, which makes it challenging to meet the training requirements of standard deep neural networks such as convolution neural network and time convolution network. Therefore, according to the data characteristics and high accuracy requirements of the nuclear power plant, a nuclear power plant accident diagnosis method based on time convolution capsule network was proposed in this paper. Firstly, the high-dimensional data obtained from the real-time monitoring of the nuclear power plant were input into the time convolution kernel to extract the depth of time-series features information of the data and reduce the difficulty of calculating. Then, the depth vector features of the data were mined through the capsule network to maximize the use of the data features of the operational information, which could also get a good convergence effect in the case of fewer accident data. Finally, the accident simulation data of the nuclear power plant were

关 键 词：核动力系统 事故诊断 胶囊网络 时间卷积网络 

分 类 号：TL334[核科学技术—核技术及应用]