基于小波分析的铁路桥梁变形监测数据处理研究
Research on Data Processing of Railway Bridge Deformation Monitoring Based on Wavelet Analysis
陈旭升1
作者信息
- 1. 中国铁路设计集团有限公司,天津 300308
- 折叠
摘要
为解决铁路桥梁自动化变形监测数据中存在噪声等问题,基于一种小波阈值去噪法,对获取的自动化变形监测数据进行去噪处理,该方法可以剔除自动化变形监测数据中的噪声,提取铁路桥梁真实变形趋势信息.首先利用Matlab构造一组包含噪声的仿真变形监测信号,通过仿真实验分析不同小波基函数、不同小波分解层数对去噪结果造成的影响,为了达到最优去噪结果,利用去噪质量综合评价指标T来选取最优小波基函数和小波分解层数.最后,为证明该去噪方法的可靠性,利用该去噪方法对某铁路桥梁静力水准仪自动化变形监测数据进行去噪处理,结果显示该方法能较好地剔除实测数据中噪声,并保留桥墩变形的细部特征.
Abstract
In order to solve the noise problem in the automatic deformation monitoring data of railway bridges,the acquired automatic deformation monitoring data was denoised based on a wavelet threshold denoising method.The method could remove noise from automated deformation monitoring data and extract the real deformation trend information of railway bridges.Firstly,a set of simulated deformation monitoring signals containing noise was constructed by the Matlab.The influences of different wavelet basis functions and different wavelet decomposition levels on the denoising results were analyzed through simulation experiments.In order to achieve the optimal denoising result,the denoising quality comprehensive evaluation index T was used to select the optimal wavelet basis function and wavelet decomposition level.Finally,to prove the reliability of the denoising method,the denoising method was used to denoise the automatic deformation monitoring data of a railway bridge static level.The results show that this method can effectively remove the noise in the measured data and retain the detailed characteristics of the bridge pier deformation.
关键词
铁路/小波分析/阈值去噪/综合评价指标/自动化监测数据Key words
railway/wavelet analysis/threshold denoising/comprehensive evaluation indicators/automated monitoring data引用本文复制引用
出版年
2024
NETL
NSTL
万方数据