桥梁长期健康监测数据的小波特征分析

Test Wavelet Features Analysis on Long-term Bridge Health Monitoring Data

桥梁健康监测是现代大跨度桥梁不可或缺的一部分,采用合适的信号处理方法分析监测数据可有效对桥梁的状态进行判断。本文基于实体桥的健康监测数据,通过傅里叶变换,得到实测温度和挠度数据是由两个相同的主要频率组成,转换周期分别对应为12小时和24小时。在此基础上对挠度和温度数据进行深层次的小波分析,挖掘挠度温度数据小波分层后高频部分和低频部分的数据特征。为探究挠度与温度场关联性,结合温度挠度数据低频部分的数据特征,分析顶底板数据挠度差和温度差随时间变化规律,验证了周期为24小时对应的挠度是由于日照温度变化引起的。提取了箱梁截面温度、挠度低频部分幅值,探讨了温度场引起的挠度幅值占总挠度幅值比重。

Operation health monitoring is an indispensable part for modern long-span bridge.The analysis of monitoring data by adopting appropriate signal processing method is effective for determining the state of bridges.In this paper,based on the bridge health monitoring data and Fourier transform,the measured temperature and deflection data are composed of two identical main frequency,and the corresponding conversion periods are12 hours and 24 hours,respectively.Based on that,the characteristic of high and low frequency of wavelet layered data is mined after deep analysis of wavelet on temperature and deflection data.To explore the correction between deflection and temperature field,the variation of both deflection and temperature differences of upper and bottom plates with time was investigated in combination with characteristics of temperature and deflection data of low frequency part.It is verified that the deflection data corresponding to the conversion period for 24 hours is caused by sunshine temperature change.Besides,the low frequency amplitude of temperature and deflection data from the steel-box girder bridge was extracted,and the proportion of deflection amplitude caused by temperature fieldin the total deflection amplitude was analyzed.