激光位移计常用于桥梁结构形变的在线监测, 对使用中的激光位移计进行在线校准, 是确保桥梁监测系统量值准确的关键。由于传感器间采样频率和时滞特征的差异, 被校准传感器的测值序列与参考值序列常常存在较大的位置偏差, 对传感器的在线校准结果产生较大的影响。为此, 提出了一种基于特征点分段的数据序列匹配方法。以简支梁模型桥实验数据为基础, 进行了激光位移计的数据匹配实验验证, 通过对两组数据序列进行特征点定位、分段及量值匹配, 解决了传统匹配方法中出现的数据错位问题。实验结果表明, 该算法对传感器采样频率偏移有较好的适应性, 针对不同场景的匹配准确率能保持在98%以上。

On-line calibration of laser displacement meter (LDM) used for monitoring bridge structures is the key to ensure the accuracy of the bridge monitoring systems. Due to the differences of sampling frequency and time delay between different sensors, the measuring value sequence of the sensor to be calibrated (SBC) often has a big deviation with the reference value sequence, which seriously impacts on the on-line calibration results. To solve this problem, a matching algorithm for data sequences based on feature points sectioning was proposed. The data matching test was conducted with bridge beam model. The mismatching problem encountered by the traditional method was well solved. The results show that the proposed algorithm has good adaptability to sampling frequency offset with a matching accuracy of above 98%.