测绘通报 ›› 2023, Vol. 0 ›› Issue (9): 100-106.doi: 10.13474/j.cnki.11-2246.2023.0272

• 学术研究 • 上一篇    下一篇

基于Savitzky-Golay平滑-小波降噪处理的桥梁结构监测数据分析方法

董是1,2, 龙志友1,2, 毕洁夫2,3, 王建伟1,2, 邵永军4, 杨超4, 左琛1,2, 张士远1,2, 万昭龙1,2   

  1. 1. 长安大学运输工程学院, 陕西 西安 710064;
    2. 道路基础设施数字化教育部工程研究中心, 陕西 西安 710064;
    3. 长安大学公路学院, 陕西 西安 710064;
    4. 陕西高速公路工程试验检测有限公司, 陕西 西安 710086
  • 收稿日期:2022-10-10 发布日期:2023-10-08
  • 通讯作者: 龙志友。E-mail:2020234028@chd.edu.cn
  • 作者简介:董是(1989—),男,博士,研究方向为交通基础设施智能运维管理。E-mail:dongshi@chd.edu.cn
  • 基金资助:
    国家自然科学基金(52108395);中国博士后科学基金(2021M692427);陕西省交通运输厅科研项目(20-03K;22-01X);浙江省交通运输科技计划项目(202316-2)

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

DONG Shi1,2, LONG Zhiyou1,2, BI Jiefu2,3, WANG Jianwei1,2, SHAO Yongjun4, YANG Chao4, ZUO Chen1,2, ZHANG Shiyuan1,2, WAN Zhaolong1,2   

  1. 1. College of Transportation Engineering, Chang'an University, Xi'an 710064, China;
    2. Engineering Research Center of Highway Infrastructure Digitalization, Ministry of Education, Xi'an 710064, China;
    3. College of Highway, Chang'an University, Xi'an 710064, China;
    4. Shaanxi Expressway Engineering Testing Inspection & Testing Co., Ltd., Xi'an 710086, China
  • Received:2022-10-10 Published:2023-10-08

摘要: 桥梁结构健康监测源信号数据存在毛刺、噪声及异常值等复杂情况,对信号数据分析、桥梁结构状态评估结果产生巨大偏差。本文提出了解决桥梁结构监测数据处理中复杂问题的系统性方法。首先,通过对比研究Savitzky-Golay(SG)滤波与信号平滑方法,并评价各方法的适用性;然后,基于复合评价指标量化选出最优小波基函数及最佳小波分解尺度;最后,采用卡尔曼滤波对同一监测项目的4个应变计数据进行融合,并用Monte Carlo仿真进行验证。研究表明,本文提出的SG平滑-小波降噪方法,可为桥梁健康监测数据处理提供较为全面、系统的参考。

关键词: 结构健康监测, Savitzky-Golay平滑, 小波降噪, 信号评价指标, 数据融合

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

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