隧道健康监测(THM)系统可确保安全运行和有效维护。然而,如何有效地处理和去噪THM收集的几个数据还有待解决,以及安全预警问题。因此,Savitzky-Golay平滑(SGS)和小波变换去噪(WTD)的集成方法用于平滑数据和过滤噪声,并提出了非均匀变异法的系数进行预警。THM数据,包括四种类型的传感器,尝试使用所提出的方法。首先,缺少值,异常值,并处理了数据中的趋势,然后SGS对数据进行了平滑处理。此外,通过选择小波基函数进行数据去噪,分解尺度,和重建。最后,非均匀变异系数用于计算黄色和红色阈值.在数据平滑中,发现SGS平滑的信噪比(SNR)和均方根误差(RMSE)优于移动平均平滑和五点三次平滑约10%和30%,分别。发现了一个有趣的现象:选择后不同小波基函数的去噪效果的最大值和最小值差异很大,信噪比相差14%,RMSE下降8%,r高达80%。发现小波基函数变化,而分解尺度始终设置在三层。SGS和WTD可以有效降低数据的复杂性,同时保留其关键特征,具有良好的去噪效果。黄色和红色警告阈值分为常规和关键控件,分别。这种预警方法极大地提高了隧道安全控制的效率。
A tunnel health monitoring (THM)
system ensures safe operations and effective maintenance. However, how to effectively process and denoise several data collected by THM remains to be addressed, as well as safety early warning problems. Thus, an integrated method for Savitzky-Golay smoothing (SGS) and Wavelet Transform Denoising (WTD) was used to smooth data and filter noise, and the coefficient of the non-uniform variation method was proposed for early warning. The THM data, including four types of sensors, were attempted using the proposed method. Firstly, missing values, outliers, and detrend in the data were processed, and then the data were smoothed by SGS. Furthermore, data denoising was carried out by selecting wavelet basis functions, decomposition scales, and reconstruction. Finally, the coefficient of non-uniform variation was employed to calculate the yellow and red thresholds. In data smoothing, it was found that the Signal Noise Ratio (SNR) and Root Mean Square Error (RMSE) of SGS smoothing were superior to those of the moving average smoothing and five-point cubic smoothing by approximately 10% and 30%, respectively. An interesting phenomenon was discovered: the maximum and minimum values of the denoising effects with different wavelet basis functions after selection differed significantly, with the SNR differing by 14%, the RMSE by 8%, and the r by up to 80%. It was found that the wavelet basis functions vary, while the decomposition scales are consistently set at three layers. SGS and WTD can effectively reduce the complexity of the data while preserving its key characteristics, which has a good denoising effect. The yellow and red warning thresholds are categorized into conventional and critical controls, respectively. This early warning method dramatically improves the efficiency of tunnel safety control.