摘要
大坝原观监测数据是掌握大坝运行性态最重要的资料,监测数据中的异常值又是分析过程中关注的重点。异常值分为两类,一类由测量误差产生,应给予剔除或补测,避免影响后续分析,另一类由于结构突变产生异常,应高度重视。目前坝工领域主要的异常值识别方法多未考虑结构异常的影响,仅从传统数学统计角度出发,造成识别准确率不高。为此,在深入研究大坝监测数据及异常值特征的基础上,首先采用稳健MM估计消除内外因正常影响,再利用剩余测值前后逐差消除稳定异常影响,最后根据极小值法对剩余值进行异常识别,通过对大坝实测数据的应用,证明了该法可以更有效、稳健地识别测量异常,避免结构稳定异常的干扰。
The original monitoring data of dams is the most important data to grasp the operation behavior of the dams,and the outliers in the data are the focus during the analysis.Outliers are divided into two categories.One category is caused by measurement errors and should be eliminated or supplemented to avoid affecting subsequent analysis.The other is caused by structural mutations and should be highly valued.At present,main outlier recognition methods in dam engineering are based on traditional mathematical statistics and do not consider the influence of structural anomalies,which results in low recognition accuracy.Therefore,based on an in-depth study of dam monitoring data and outlier characteristics,this paper first employs robust MM estimation to eliminate the normal influence of internal and external factors and then adopts the residual measured value to eliminate the stable abnormal influence by difference before and after.Finally,according to the minimum value method,outlier identification is conducted on the residual values.The application of the measured dam data proves that the proposed method can identify the measurement outliers more effectively and robustly,and avoid the interference of structural stability anomalies.
作者
梁汇彬
张瀚
张林松
曹宇鑫
周靖人
LIANG Huibin;ZHANG Han;ZHANG Linsong;CAO Yuxin;ZHOU Jingren(State Key Laboratory of Hydraulics and Mountain River Engineering,Sichuan University,Chengdu 610065,China;College of Water Resources&Hydropower,Sichuan University,Chengdu 610065,China)
出处
《人民珠江》
2024年第3期138-145,共8页
Pearl River
基金
四川省科技厅重点研发项目(2022YFS0535)。
关键词
异常值识别
时间序列数据
稳健估计
大坝监测
变量分离
outlier detection
time series data
robust estimation
dam monitoring
variable separation
