变分模态分解结合样本熵的变形监测数据降噪

针对变形监测数据中包含的噪声成分难以有效滤除，导致预测结果精度不理想的问题，提出一种应用于变形监测领域的新降噪方法。首先利用变分模态分解（VMD）将原始监测序列分解为k个不同中心频率的带限固有模态函数（BIMF），再将样本熵（SE）大于设定阈值的高频BIMF作为噪声成分剔除，最后重构余下的BIMF获得降噪序列。仿真算例和工程实例检验的结果表明，新方法与EEMD及CEEMD方法相比各评价指标均为最优，降噪结果可为形变分析和预测提供可靠依据。

Deformation Monitoring Data De-Noising Method Based on Variational Mode Decomposition Combined with Sample Entropy

The noise components contained in the deformation monitoring data are difficult to effectively filter out, which leads to the unsatisfactory accuracy of the prediction results, so we propose a new noise reduction method applied in the field of deformation monitoring. First, we use VMD to decompose the original monitoring sequence into k band-limited natural mode functions(BIMF) with different center frequencies, and then directly remove the high-frequency BIMF with sample entropy greater than a set threshold as the noise component, and finally reconstruct the remaining BIMF to obtain the noise reduction sequence. The effectiveness and feasibility of the new method are verified by simulation and engineering examples; the results show that compared with EEMD and CEEMD, the new method has the best evaluation index, and the noise reduction results obtained by the VMD-SE method can provide a reliable basis for further deformation analysis and prediction.