基于小波变换的桥梁动态称重系统车轴高精度识别研究  

Improved Identification of Vehicular Axles in BWIM System Based on Wavelet Transform

作　　者：赵华[1] 谭承君[1] 张龙威[1] 乔东钦 

机构地区：[1]湖南大学土木工程学院风工程与桥梁工程湖南省重点实验室,湖南长沙410082

出　　处：《湖南大学学报（自然科学版）》2016年第7期111-119,共9页Journal of Hunan University:Natural Sciences

基　　金：国家自然科学基金资助项目(51178178);湖南省自然科学基金资助项目(13JJ2019);高等学校博士学科点专项科研基金资助项目(20110161120025)~~

摘　　要：首先利用小波变换对一不能明显识别车轴信息的数值仿真信号进行处理,证明小波变换能够高效放大车轴经过传感器时产生的不连续变化斜率,从而识别出车轴信息.然后基于实桥测试,对那些不能直接识别出车辆信息的FAD信号,通过联合控制最小Shannon熵值和最大相关系数选取最适变换尺度和最适变换小波函数进行小波变换.分析结果表明:对于不能直接识别出车辆信息的FAD信号,小波变换也能准确地识别车辆行驶速度、车轴数目以及车轴间距.小波变换可提高桥梁动态称重(BWIM)系统车轴识别的效率及精度,为将BWIM系统发展为超载车辆控制的有效工具提供技术支撑.In this study, wavelet transform was firstly applied to deal with a numerically simulated sig- nal that was unable to obviously identify axle information. The analysis result showed that the wavelet transform was able to magnify the slope discontinuities so as to accurately identify the silhouette of passing vehicles. Subsequently, based on the field-tested FAD signals through which the vehicle configuration was difficult to be directly identified, the most appropriate transform scales and the best suitable wavelet func- tion performing wavelet transform were selected from the minimum Shannon entropy and maximum corre- lation. The results demonstrated that the wavelet transform in pattern recognition effectively identified the vehicle configuration （including vehicle velocity, axle numbers, and axle spacing）, especially for the uni- dentified FAD signals. Therefore, wavelet domain analysis can effectively improve the efficiency and accu- racy for the vehicular axle identification in BWIM system, of BWlM system in controlling and monitoring overweight and it is beneficial for the successful application vehicles.

关 键 词：桥梁动态称重 车轴识别 小波变换 小波函数选取 变换尺度 

分 类 号：U491[交通运输工程—交通运输规划与管理] TN911.7[交通运输工程—道路与铁道工程]