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作 者:王超[1] 刘小强[1] 王立新[2] WANG Chao;LIU Xiaoqiang;WANG Lixin(School of Information Engineering,Chang’an University,Xi’an 710064,China;China Railway First Survey and Design Institute Group Co.,Ltd.,Xi’an 710043,China)
机构地区:[1]长安大学信息工程学院,陕西西安710064 [2]中铁第一勘察设计院集团有限公司,陕西西安710043
出 处:《电子设计工程》2022年第2期174-179,共6页Electronic Design Engineering
基 金:国家重点研发计划(2018YFC0808706);中铁一院科研项目资助(19-42-02)。
摘 要:在结构健康监测中,为消除温度效应对基坑钢支撑应变的影响,提出了基于数据融合与小波分解的温度效应消除算法,通过改进的卡尔曼滤波融合算法将多通道应变与温度数据进行组间融合,得到一组应变与温度的融合数据。对两种融合数据进行小波分解,通过相关性分析从应变融合数据中提取出由温度效应引起的应变量,利用二次光滑局部线性回归算法建立温度值与应变量之间的回归模型,达到消除温度效应的目的。结合某地铁基坑钢支撑应变与温度的监测数据分析,该算法可有效提取并消除应变中的温度效应,得到基坑结构自身引起的应变量。仿真实验分析表明,该算法相对小波相关性分析法、局部加权回归法、多元回归算法的误差分别降低了71%、69%、16%。In order to eliminate the influence of temperature effect on the steel beam strain of retaining structures for deep excavations,a temperature effect elimination algorithm based on data fusion and wavelet decomposition is proposed. The strain data and temperature data monitored by multi-source sensors are fused by improved Kalman filter fusion algorithm. The temperature fusion results and strain fusion results are obtained. The two kinds of fusion data are decomposed by wavelet. Through the correlation analysis of wavelet components between temperature and strain,the strain caused by temperature effect is extracted from the strain fusion data. By establishing the regression model of temperature and strain,the temperature effect is eliminated. After the monitoring data analysis of the strain and temperature from a subway foundation pit steel support,the scheme can effectively eliminate the temperature effect in the strain,and obtain the strain change of the retaining structures. Compared with the existing wavelet correlation analysis method,local weighted regression method and multiple regression algorithm,the error of this algorithm is reduced by 71%,69% and 16%.
关 键 词:结构健康监测 应变监测 温度效应 数据融合 小波分解 二次光滑局部线性回归
分 类 号:TN911.6[电子电信—通信与信息系统]
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