长江科学院院报 ›› 2020, Vol. 37 ›› Issue (6): 77-80.DOI: 10.11988/ckyyb.20190335

• 工程安全与灾害防治 • 上一篇    下一篇

基于M估计量及标准四分位间距的安全监测数据异常识别的改进方法

杨哲1,2, 李艳玲1,2, 张鹏3, 卢祥1,2, 李兴1,2   

  1. 1.四川大学 水力学与山区河流开发保护国家重点实验室,成都 610065;
    2.四川大学 水利水电学院,成都 610065;
    3.中国市政工程西南设计研究总院有限公司 第一设计研究院,成都 610081
  • 收稿日期:2019-03-28 出版日期:2020-06-01 发布日期:2020-06-21
  • 通讯作者: 李艳玲(1975-),女,四川浦江人,教授,博士,研究方向为工程安全监测与反演分析、工程安全管理。E-mail:396184191@qq.com
  • 作者简介:杨 哲(1992-),男,湖北天门人,硕士研究生,研究方向为水工结构。E-mail:791582000@qq.com

An Improved Method of Anomaly Recognition of Dam Safety Monitoring Data Based on M-Estimator and Standard Quartile Range

YANG Zhe1,2, LI Yan-ling1,2, ZHANG Peng3, LU Xiang1,2, LI Xing1,2   

  1. 1. State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, China;
    2. School of Water Resources and Hydropower, Sichuan University, Chengdu 610065, China;
    3. No. 1 Design and Research Sub-institute, Southwest Municipal Engineering Design & Research Institute of China, Chengdu 610081, China
  • Received:2019-03-28 Online:2020-06-01 Published:2020-06-21

摘要: 水电工程安全监测数据的异常识别是科学评价大坝安全性态的前提。为了解决传统的准则运用于“台阶型”、“震荡型”监测数据序列异常在线识别时极易出现的漏判问题,引入Andrews M估计量和标准四分位间距替代传统准则中的总体位置参数和总体尺度参数,构建了新的判别准则。工程实践及敏感性分析表明:该方法能有效消除“台阶型”和“震荡型”离群点对识别结果带来的不利影响;抗离群点影响的比例提升到25%,异常识别的准确性及可靠性提升明显。研究成果可为水电工程安全状况和运行性态的评价提供参考。

关键词: 异常值识别, 准则, M估计量, 标准四分位间距, 大坝安全性态

Abstract: Anomaly recognition of safety monitoring data of hydropower station is a prerequisite for scientific evaluation of dam safety. Traditional 3σ criterion is prone to cause miss judgment when applied to the online anomaly identification of “step type” and “oscillating type” monitoring data series. In view of this, we established an improved criterion by replacing the general position parameter and general scale parameter in the 3σ criteria with Andrews M-estimator and standard quartile range. Engineering practice and sensitivity analysis prove that the method could effectively eliminate the adverse effects of anomalies on the recognition results. The proportion of anti-anomaly amounts 25%, and the accuracy and reliability of anomaly recognition are improved obviously.

Key words: anomaly identification, 3σ criterion, M-estimator, standard quartile range, dam safety condition

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