Analysis method for bridge structure monitoring data based on Savitzky-Golay smoothing-wavelet noise reduction processing

doi:10.13474/j.cnki.11-2246.2023.0272

Abstract

Abstract: Bridge structural health monitoring signal data have some complexities such as burrs, noise and outliers, which produce huge deviations in signal data analysis and assessment of bridge structural status. In this paper, we propose a systematic approach to solve the complex problems in bridge structure monitoring data processing. Firstly, the applicability of each method is evaluated by comparing Savitzky-Golay (SG) filtering and traditional smoothing methods. Secondly, the optimal wavelet basis function and the optimal wavelet decomposition scale are quantified and selected based on the composite evaluation index. Finally, the Kalman filter is used to fuse four strain gage data of the same monitoring project and verified by Monte Carlo simulation. The study shows that the SG smoothing-wavelet noise reduction method is proposed in this paper can provide a more comprehensive and systematic reference for bridge health monitoring data processing.

Key words: structural health monitoring, Savitzky-Golay smoothing, wavelet noise reduction, signal evaluation metrics, data fusion

CLC Number:

P258

DONG Shi, LONG Zhiyou, BI Jiefu, WANG Jianwei, SHAO Yongjun, YANG Chao, ZUO Chen, ZHANG Shiyuan, WAN Zhaolong. Analysis method for bridge structure monitoring data based on Savitzky-Golay smoothing-wavelet noise reduction processing[J]. Bulletin of Surveying and Mapping, 2023, 0(9): 100-106.

References

[1] 王东升,童磊,王荣霞,等.大跨PC连续刚构桥抗震研究进展综述[J/OL].西南交通大学学报:1-16[2022-10-02].http://kns.cnki.net/kcms/detail/51.1277.U.20211020.0828.002.html.
[2] 袁长征,滕德贵,李超,等. 重庆嘉华大桥健康监测系统设计与建设[J]. 测绘通报,2021(S2):116-120.
[3] 中国工程建设标准化协会. 大跨度桥梁结构健康监测系统预警阈值标准:T/CECS 529-2018[S]. 北京:中国建筑工业出版社,2018.
[4] 李征,房宏才,柯熙政,等. 滑动平均法在MEMS陀螺信号趋势项提取中的应用[J]. 电子测量与仪器学报,2019,33(7):43-49.
[5] 樊学平,周衡,刘月飞. 桥梁时变可靠性的多过程贝叶斯动态混合预测[J/OL]. 吉林大学学报(工学版):1-11[2022-10-02].http://xuebao.jlu.edu.cn/gxb/CN/article/showOldPictureList.do.
[6] SAVITZKY A,GOLAY M J E. Smoothing and differentiation of data by simplified least squares procedures[J]. Analytical Chemistry,1964,36(8):1627-1639.
[7] 吴迪,陈健,石满,等. 基于Savitzky-Golay滤波算法的FY-2F地表温度产品时间序列重建[J]. 国土资源遥感,2019,31(2):59-65.
[8] 汪松,王斌,刘长征,等. 利用Landsat时序NDVI数据进行新疆石河子垦区灌溉作物分类[J]. 测绘通报,2016(9):56-59.
[9] 鲁一冰,刘文清,张玉钧,等. 一种自适应层进式Savitzky-Golay光谱滤波算法及其应用[J]. 光谱学与光谱分析,2019,39(9):2657-2663.
[10] 宁鸿章,谭鑫,李宇航,等. 空-谱维联合Savitzky-Golay高光谱滤波算法及其应用[J]. 光谱学与光谱分析,2020,40(12):3699-3704.
[11] 董博. 小波变换技术在变形监测中的应用[J]. 测绘通报,2016(S2):150-152.
[12] NGELJARATAN L,MOUSTAFA M A,PEKCAN G. A compressive sensing method for processing and improving vision-based target-tracking signals for structural health monitoring[J]. Computer-Aided Civil and Infrastructure Engineering,2021,36(9):1203-1223.
[13] LI Tianqi,HE Qingbo,PENG Zhike.Parameterized resampling time-frequency transform[J].IEEE Transactions on Signal Processing,2022,70:5791-5805.
[14] 石安平,周吕,王成,等. 结合EMD与小波阈值去噪的GB-RAR桥梁监测信号去噪[J]. 测绘通报,2022(S2):227-232.
[15] 陈竹安,胡志峰. 小波阈值改进算法的遥感图像去噪[J]. 测绘通报,2018(4):28-31.
[16] 钟建军,宋健,由长喜,等. 基于信噪比评价的阈值优选小波去噪法[J]. 清华大学学报(自然科学版),2014,54(2):259-263.
[17] 朱建军,章浙涛,匡翠林,等. 一种可靠的小波去噪质量评价指标[J]. 武汉大学学报(信息科学版),2015,40(5):688-694.
[18] 岳胜杰,王红旗,刘群坡,等. 结合扩展卡尔曼滤波与基于点线的最近点迭代扫描匹配算法的机器人位姿自适应估计[J]. 测绘通报,2022(7):49-53.
[19] 刘新宇,王勇,唐超. 静力水准自动化测量技术应用于地铁隧道结构监测[J]. 测绘通报,2021(8):69-73.
[20] LI Shoupeng,MU Rongjun,CUI Naigang. Simplified augmented cubature information filtering and multi-sensor fusion for additive noise systems[J]. Aerospace Science and Technology,2022,123:107445.
[21] SADRIAN ZADEH D,MOSHIRI B,ABEDINI M,et al. Supervised learning for more accurate state estimation fusion in IoT-based power systems[J]. Information Fusion,2023,96:1-15.