摘要
针对核动力系统监测参数受噪声干扰出现随机丢失,影响操纵员判断事故严重程度的问题,提出了容忍参数缺失的破口评估模型。选定已知破口大小的多元序列作为标准序列,并在标准序列上按事故机理选定若干采样点,对待诊断多元时间序列上各时间点使用滑动动态时间弯曲算法寻找与标准序列采样点的最小累积距离,将得到的最小累积距离作为破口评估模型的特征值,使用支持向量机作为预测模型对破口进行评估,并通过集成学习策略优化诊断结果。以右侧主蒸汽管道破口为例进行验证,结果表明,该方法对待测序列的完整性要求不高,参数随机缺失的破口评估误差在10%以内,能够更好地辅助操纵员进行破口的评估。
The monitoring parameters of the nuclear power system are randomly lost due to noise interference,which affects the judgement of the operators onthe severity of the accident.A diagnosis model of fracture size with tolerance parameter loss is proposed.The multiple time series which fracture size is known is selected as the standard series,and several sampling sites are built on the standard series based on the accident mechanism.The sliding dynamic time warping algorithm is adopted to find the minimum cumulative distance between the diagnosed multivariate time series and the standard sampling site,and all the minimum cumulative distances obtained are taken as the characteristic values of the fracture diagnosis model.The support vector machine is used as the prediction model to predict the size of the fracture,and the ensemble learning strategy is used to optimize the diagnosis results.Taking the right main steam pipeline as an example for verification,the results show that this method does not have high requirements for the integrity of sequencing sequences,and the evaluation error of the fracture with random loss of parameters is within 10%,which makes it better for the auxiliary operator to conduct the evaluation of the fracture.
作者
赵鑫
蔡琦
张黎明
赵新文
王晓龙
李海翠
Zhao Xin;Cai Qi;Zhang Liming;Zhao Xinwen;Wang Xiaolong;Li Haicui(College of Nuclear Science and Technology,Naval University of Engineering,Wuhan,430033,China)
出处
《核动力工程》
EI
CAS
CSCD
北大核心
2020年第6期187-193,共7页
Nuclear Power Engineering
基金
核反应堆系统设计技术国防重点实验室基金(HT-JXYY-02-2014002)。
关键词
动态弯曲
故障诊断
随机缺失数据
支持向量机
集成学习
Dynamic time warping
Fracture diagnosis
Random missing data
Support vector machine
Ensemble learning
