隧道结构在线监测数据分析方法研究

An analysis method for the online monitoring data of tunnel structure

数据管理系统完成监测数据预处理,在健康监测系统中具有重要地位。通过比选利用罗曼诺夫斯基准则（t检验法）进行监测数据粗差检测。小波降噪方法可以有效去除监测数据中由于环境等因素引起的误差（噪声）,获得更真实的监测数据,但目前如何选取阈值函数、阈值和分解层数等问题未形成统一认识。利用武汉长江隧道健康监测系统中39组监测数据,从统计学的角度对比分析了不同阈值和阈值函数组合下的小波降噪效果,结果表明,选择Rigrsure阈值和硬阈值函数进行4~5层分解降噪效果最好,将所得的研究结果应用于武汉长江隧道健康监测系统中其他同期监测数据的降噪中取得了满意的效果。在此基础上进一步研究表明,相比于原始监测数据,利用降噪后数据预警可以有效避免虚警的产生。最后提出利用小波降噪和最小二乘法结合进行监测数据预测的方法,实践表明,预测结果准确可靠。

Data preprocessing completed in a data management system plays an important role in structural health monitoring. The Romanowski guideline（t-test） is selected to eliminate the gross error through comparison. The wavelet denoising method can effectively remove the noise（or error） caused by environmental factors, and yield more reliable monitoring data. However, a consensus has not been achieved on how to select the threshold function, the threshold and the number of decomposition layers. Using the 39 groups of monitoring data obtained in the health monitoring of the Yangtze River tunnel, we analyze the effects of various combinations of thresholds and threshold functions on the results of the wavelet denoising method. It is shown that the denoising effect via 4-5 decomposition layers is the best using the combination of Rigrsure threshold and hard threshold function. A satisfactory result is achieved when the above research are applied to denoise the corresponding period monitoring data. Based on these results, it is shown that the denoised data can effectively prevent the generation of false alarm in the early warning compared to the original data. Finally, a method based on the combination of the wavelet denoising and least squares method is proposed to predict the monitoring data.