摘要
为解决传统基于深度学习(DL)的智能故障诊断模型无法监测系统未知异常运行事件的问题。本研究基于变分推断的概率深度神经网络(VI-PDNN)构建反应堆冷却剂系统智能诊断框架,对未知异常运行事件类别实现诊断,同时量化评估输出结果的不确定性。该框架能够有效利用已知与未知运行事件的不确定性差异实现对未知异常运行事件的有效监测预警。最后,基于已建立反应堆模拟机仿真数据对本文提出方法进行验证。研究结果表明,提出的方法不仅能够针对系统已知事件获取较高的诊断精度,同时能有效监测预警未知异常运行事件,为实际环境下反应堆系统运行实时智能诊断与监测提供一种有效技术手段。
In order to solve the problem that the traditional deep learning(DL)-based intelligent fault diagnosis model cannot monitor the unknown abnormal operation events,this work constructs an intelligent diagnosis framework for reactor coolant system based on the Variational Inferencebased Probabilistic Deep Neural Network(VI-PDNN),enabling the diagnosis of unknown abnormal operation event categories while quantitatively evaluating the uncertainty of the output results.The framework can effectively utilize the uncertainty difference between known and unknown operating events to achieve effective monitoring and warning of unknown abnormal operating events.Finally,the proposed methodology is validated based on simulation data from an established reactor simulator.The results show that the proposed method not only obtains high diagnostic accuracy for known events,but also effectively monitors and warns against unknown abnormal events,providing an effective means for real-time intelligent diagnosis and monitoring of reactor system operation in real environment.
作者
姚源涛
者娜
雍诺
夏冬琴
戈道川
郁杰
Yao Yuantao;Zhe Na;Yong Nuo;Xia Dongqin;Ge Daochuan;Yu Jie(Institute of Nuclear Energy Safety Technology,Hefei Institute of Physical Sciences,Chinese Academy of Sciences,Hefei,230031,China;Nuclear Power Institute of China,Chengdu,610213,China)
出处
《核动力工程》
北大核心
2025年第2期248-254,共7页
Nuclear Power Engineering
基金
中国博士后科学基金面上项目(2022M713186)
国家自然科学基金(72204246)。
关键词
反应堆冷却剂系统
未知异常运行事件
智能故障诊断与监测
不确定性评估
Reactor coolant system
Unknown abnormal operation event
Intelligent fault diagnosis and monitoring
Uncertainty evaluation
