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

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

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。

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.