摘要
结构监测数据往往因设备异常或环境干扰而产生尖点异常数据,人工后处理工作量大,且干扰实时报警的准确性。基于滑动平均滤波器与3σ准则,提出了双滑动窗口判别法进行结构监测数据的跳值异常识别,并按3σ准则生成异常数据的合理代表值,将异常数据处理为合理数据。滑动滤波器考虑了施工步监测数据的阶跃现象,有效解决了因施工步引起的监测数据突变而造成的误判问题。通过杭州西站施工和运营过程中的实际监测数据对提出的方法进行验证,计算结果表明,双窗口滑动平均滤波器能够快速且有效识别监测数据中的跳点异常数据,并避免对施工步阶跃数据的误判。
Due to abnormal equipment or environmental noise,cusp anomaly data usually occurs in structural monitoring data.Manual handing is time-consuming,and cusp anomaly data disturbs the accuracy of alarm.Based on moving average filter and 3σcriterion,dual-window sliding filter was used for jump value anomaly recognition in structural monitoring.Abnormal data was translated to reasonable data by reasonable representative value of abnormal data based on 3σcriterion.The phenomenon of the jump of construction data was considered in moving average filter.Thus,misjudgment was solved.The proposed method was verified by real monitoring data in Hangzhouxi Railway Station.The results indicated that dual-window sliding filter could identify the abnormal jump point efficiently and avoid misjudgment by the phenomenon of the jump of construction data.
作者
吴奎
WU Kui(China Railway Siyuan Survey and Design Group Co.,Ltd.,Wuhan 430063,China)
出处
《工业建筑》
北大核心
2023年第2期37-41,共5页
Industrial Construction
关键词
滑动平均滤波器
异常数据
监测
跳点
moving average filter
abnormal data
monitoring
jump point
