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混凝土坝变形监测时序中异常突变值的优化诊断
陈良捷1, 魏博文2, 戴国强1, 林太清1
1.江西省水利科学院;2.南昌大学建筑工程学院
摘要:
混凝土坝变形监测时序因观测设施故障及人工采集等不确定因素而存有非正常突变现象,致使后续正反分析及综合评判等工作难度剧增。为此,在结合经验小波变换(Empirical Wavelet Transform, EWT)理论与局部离群因子(Local Outlier Factor, LOF)识别机制的基础上,通过引入箱形图方法重新定义检测模型的判断阈值,有效规避了人为主观设定带来的诸多干扰因素,据此构建了用以客观还原监测信息特征的异常点诊疗体系。工程实例分析表明,文中所提方案具有精准优异的离群子集信号识别能力以及完备的理论支撑,适合复杂内外多源环境驱动下混凝土坝变形监测信号的前期优化处理。
关键词:  混凝土坝变形  监测时序  异常值诊断  局部离群因子  经验小波变换
DOI:
分类号:
基金项目:国家自然科学基金资助项目(51869011, 52169025); 江西省水利厅科技项目(202123BZKT08, 202224ZDKT04)
Optimal Diagnosis of Abnormal Mutation Value in Deformation Monitoring Sequence of Concrete Dam
CHEN Liang-Jie1, WEI Bo-Wen2, DAI Guo-Qiang1, LIN Tai-Qing1
1.Jiangxi Academy of Water Science and Engineering;2.School of Civil Engineering and Architecture,Nanchang University
Abstract:
The deformation monitoring sequence of concrete dam has abnormal mutation due to uncertain factors such as failure of observation facilities or manual collection, which increases the difficulty of subsequent analysis and comprehensive evaluation. Therefore, on the basis of empirical wavelet transform (EWT) theory and local outlier factor (LOF) recognition mechanism, the boxplot method is introduced to redefine the judgment threshold of the detection model, which effectively avoids many interference factors caused by human subjective setting. Based on this, an outlier diagnosis and treatment system is constructed to objectively restore the characteristics of monitoring information. The engineering example shows that the proposed scheme has accurate and excellent signal recognition ability of outlier subset and complete theoretical support, which is suitable for the early optimization of the deformation monitoring signal of concrete dam driven by complex internal and external multi-source environment.
Key words:  deformation of concrete dam  monitoring sequence  outliers detection  local outlier factor  empirical wavelet transform
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