激光位移计在线校准数据序列匹配方法
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摘要
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%.
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%.
引文
[1] Sun Xingwei, Yu Xinyu, Dong Zhixu, et al. High accuracy measurement model of laser triangulation method[J]. Infrared and Laser Engineering, 2018, 47(9):0906008.(in Chinese)孙兴伟,于欣玉,董祉序,等.激光三角法高精度测量模型[J].红外与激光工程, 2018, 47(9):0906008.
[2] Zhang Benniu, Li Xingxing, Song Jun, et al.Application study on laser projection sensor for bridge deflection detection[J]. Journal of Transduction Technology, 2009, 22(5):755-759.(in Chinese)张奔牛,李星星,宋军,等.激光投射式位移传感技术在桥梁挠度检测中的应用研究[J].传感技术学报, 2009, 22(5):755-759.
[3] Chen Ji, Wang Xin, Cao Jiuda, et al. Development of high-speed CCD laser displacement sensor[J]. Optics and Precision Engineering, 2008, 16(4):611-616.(in Chinese)陈骥,王鑫,曹久大,等.高速CCD激光位移传感器[J].光学精密工程, 2008, 16(4):611-616.
[4] Li Bing, Sun Bin, Chen Lei, et al. Application of laser displacement sensor to free-form surface measurement[J]. Optics and Precision Engineering, 2015, 23(7):1939-1947.(in Chinese)李兵,孙彬,陈磊,等.激光位移传感器在自由曲面测量中的应用[J].光学精密工程, 2015, 23(7):1939-1947.
[5] Wang Shuai, Xia Jiabin, Yao Qifeng, et al. Fore optical system design for remote laser Raman spectrum detection system[J]. Infrared and Laser Engineering,2018, 47(4):0418004.(in Chinese)王帅,夏嘉斌,姚齐峰,等.远程激光拉曼光谱探测系统前置光学系统设计[J].红外与激光工程, 2018, 47(4):0418004.
[6] Zhang Shuang, Zhu Wanbin, Li Jian, et al. Design of micro-optical system for laser displacement sensor sensing probe[J]. Chinese Optics, 2018, 11(6):1001-1010.(in Chinese)张爽,朱万彬,李健,等.激光位移传感器传感探头微小型光学系统设计[J].中国光学, 2018, 11(6):1001-1010.
[7] Yin Yongkai, Liu Xiaoli, Li Ameng, et al. Sub-pixel location of circle target and its application[J]. Infrared and Laser Engineering, 2008, 37(S1):51-54.(in Chinese)殷永凯,刘晓利,李阿蒙,等.圆形标志点的亚像素定位及其应用[J].红外与激光工程, 2008, 37(s1):51-54.
[8] Lan Zhangli, Zhang Hong, Zhou Jianting. Novel system for measuring bridge deflections based on laser and video[J]. Chinese Journal of Scientific Instrument,2009, 30(11):2405-2410.(in Chinese)蓝章礼,张洪,周建庭.基于激光和视频的桥梁挠度测量新系统[J].仪器仪表学报, 2009, 30(11):2405-2410.
[9] Yuan Xingqi, Chen Kai, Cui Jianjun, et al. Calibration method of strain sensor metrological characteristics[J]. Acta Metrologica Sinica, 2018, 39(6):836-841.(in Chinese)袁兴起,陈恺,崔建军,等.应变传感器计量特性的校准方法[J].计量学报, 2018, 39(6):836-841.
[10] Lv Qiang, Li Wenhao, Bayanheshig, et al. Interferometric precision displacement measurement system based on diffraction grating[J]. Chinese Optics, 2017, 10(1):39-50.(in Chinese)吕强,李文昊,巴音贺希格,等.基于衍射光栅的干涉式精密位移测量系统[J].中国光学, 2017, 10(1):39-50.
[11] Huffman J, Xiao F, Chen G, et al. Detection of soilabutment interaction by monitoring bridge response using vehicle excitation[J]. Journal of Civil Structural Health Monitoring, 2015, 5(4):389-395.
[12] Giorgino T. Computing and visualizing dynamic time warping alignments in R:the dtw package[J]. Journal of Statistical Software, 2009, 31(7):1-24.
[13] Li Zhengxin, Zhang Fengming, Li Kewu. DTW based pattern matching method for multivariate time series[J]. Pattern Recognition and Artificial Intelligence,2011, 24(3):425-430.(in Chinese)李正欣,张凤鸣,李克武.基于DTW的多元时间序列模式匹配方法[J].模式识别与人工智能, 2011, 24(3):425-430.
[14] Li Junkui. Research on time series similarity problem[D]. Wuhan:Huazhong University of Science and Technology, 2008.(in Chinese)李俊奎.时间序列相似性问题研究[D].武汉:华中科技大学, 2008.
[15] Marteau P. Time warp edit distance with stiffness adjustment for time series matching[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2):306-318.
[16] Chen Y, Liu X, Li X, et al. Delineating urban functional areas with building-level social media data:A dynamic time warping(DTW)distance based kmedoids method[J]. Landscape and Urban Planning,2017, 160:48-60.
[17] Wan Yuqing, Gong Xueyuan, Si Yain-Whar. Effect of segmentation on financial time series pattern matching[J]. Applied Soft Computing, 2016, 38:346-359.
[18] Shokoohi-Yekta M, Hu B, Jin H, et al. Generalizing DTW to the multi-dimensional case requires an adaptive approach[J]. Data Mining and Knowledge Discovery, 2017, 31(1):1-31.
[2] Zhang Benniu, Li Xingxing, Song Jun, et al.Application study on laser projection sensor for bridge deflection detection[J]. Journal of Transduction Technology, 2009, 22(5):755-759.(in Chinese)张奔牛,李星星,宋军,等.激光投射式位移传感技术在桥梁挠度检测中的应用研究[J].传感技术学报, 2009, 22(5):755-759.
[3] Chen Ji, Wang Xin, Cao Jiuda, et al. Development of high-speed CCD laser displacement sensor[J]. Optics and Precision Engineering, 2008, 16(4):611-616.(in Chinese)陈骥,王鑫,曹久大,等.高速CCD激光位移传感器[J].光学精密工程, 2008, 16(4):611-616.
[4] Li Bing, Sun Bin, Chen Lei, et al. Application of laser displacement sensor to free-form surface measurement[J]. Optics and Precision Engineering, 2015, 23(7):1939-1947.(in Chinese)李兵,孙彬,陈磊,等.激光位移传感器在自由曲面测量中的应用[J].光学精密工程, 2015, 23(7):1939-1947.
[5] Wang Shuai, Xia Jiabin, Yao Qifeng, et al. Fore optical system design for remote laser Raman spectrum detection system[J]. Infrared and Laser Engineering,2018, 47(4):0418004.(in Chinese)王帅,夏嘉斌,姚齐峰,等.远程激光拉曼光谱探测系统前置光学系统设计[J].红外与激光工程, 2018, 47(4):0418004.
[6] Zhang Shuang, Zhu Wanbin, Li Jian, et al. Design of micro-optical system for laser displacement sensor sensing probe[J]. Chinese Optics, 2018, 11(6):1001-1010.(in Chinese)张爽,朱万彬,李健,等.激光位移传感器传感探头微小型光学系统设计[J].中国光学, 2018, 11(6):1001-1010.
[7] Yin Yongkai, Liu Xiaoli, Li Ameng, et al. Sub-pixel location of circle target and its application[J]. Infrared and Laser Engineering, 2008, 37(S1):51-54.(in Chinese)殷永凯,刘晓利,李阿蒙,等.圆形标志点的亚像素定位及其应用[J].红外与激光工程, 2008, 37(s1):51-54.
[8] Lan Zhangli, Zhang Hong, Zhou Jianting. Novel system for measuring bridge deflections based on laser and video[J]. Chinese Journal of Scientific Instrument,2009, 30(11):2405-2410.(in Chinese)蓝章礼,张洪,周建庭.基于激光和视频的桥梁挠度测量新系统[J].仪器仪表学报, 2009, 30(11):2405-2410.
[9] Yuan Xingqi, Chen Kai, Cui Jianjun, et al. Calibration method of strain sensor metrological characteristics[J]. Acta Metrologica Sinica, 2018, 39(6):836-841.(in Chinese)袁兴起,陈恺,崔建军,等.应变传感器计量特性的校准方法[J].计量学报, 2018, 39(6):836-841.
[10] Lv Qiang, Li Wenhao, Bayanheshig, et al. Interferometric precision displacement measurement system based on diffraction grating[J]. Chinese Optics, 2017, 10(1):39-50.(in Chinese)吕强,李文昊,巴音贺希格,等.基于衍射光栅的干涉式精密位移测量系统[J].中国光学, 2017, 10(1):39-50.
[11] Huffman J, Xiao F, Chen G, et al. Detection of soilabutment interaction by monitoring bridge response using vehicle excitation[J]. Journal of Civil Structural Health Monitoring, 2015, 5(4):389-395.
[12] Giorgino T. Computing and visualizing dynamic time warping alignments in R:the dtw package[J]. Journal of Statistical Software, 2009, 31(7):1-24.
[13] Li Zhengxin, Zhang Fengming, Li Kewu. DTW based pattern matching method for multivariate time series[J]. Pattern Recognition and Artificial Intelligence,2011, 24(3):425-430.(in Chinese)李正欣,张凤鸣,李克武.基于DTW的多元时间序列模式匹配方法[J].模式识别与人工智能, 2011, 24(3):425-430.
[14] Li Junkui. Research on time series similarity problem[D]. Wuhan:Huazhong University of Science and Technology, 2008.(in Chinese)李俊奎.时间序列相似性问题研究[D].武汉:华中科技大学, 2008.
[15] Marteau P. Time warp edit distance with stiffness adjustment for time series matching[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(2):306-318.
[16] Chen Y, Liu X, Li X, et al. Delineating urban functional areas with building-level social media data:A dynamic time warping(DTW)distance based kmedoids method[J]. Landscape and Urban Planning,2017, 160:48-60.
[17] Wan Yuqing, Gong Xueyuan, Si Yain-Whar. Effect of segmentation on financial time series pattern matching[J]. Applied Soft Computing, 2016, 38:346-359.
[18] Shokoohi-Yekta M, Hu B, Jin H, et al. Generalizing DTW to the multi-dimensional case requires an adaptive approach[J]. Data Mining and Knowledge Discovery, 2017, 31(1):1-31.