基于小波变换的桥梁动态称重系统车轴高精度识别研究

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 signal 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 function performing wavelet transform were selected from the minimum Shannon entropy and maximum correlation. 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 unidentified FAD signals. Therefore, wavelet domain analysis can effectively improve the efficiency and accuracy for the vehicular axle identification in BWIM system, and it is beneficial for the successful application of BWIM system in controlling and monitoring overweight vehicles.