The Feasibility Study on Settlement Monitoring of a Parallel Combination Prediction Method Based on ELMD
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摘要: 时频分解方法局部均值分解（local mean decomposition，LMD）在沉降监测中已经得到了应用，但在使用中会出现模态混叠现象。总体局部均值分解（ensemble local mean decomposition，ELMD）通过添加辅助噪声可以抑制局部均值分解过程中出现的模态混叠现象。提出了一种基于ELMD的并联式组合沉降预测方法，结合高速铁路某桥梁实际监测数据，在对ELMD模型进行仿真分析的基础上，分别使用ELMD和LMD将一组离散非线性信号分解为3个PF分量和1个剩余分量，并利用支持向量机和卡尔曼滤波进行预测验证。结果表明：使用ELMD进行分解的过程中能够很好地抑制LMD方法中出现的模态混叠问题。在预报精度方面，基于ELMD的并联式组合模型的平均相对误差可以达到8.3%，可为沉降监测的预报工作提供参考和借鉴。Abstract: In the field of time-frequency decomposition, the Local Mean Decomposition(LMD) method is applied in settlement monitoring, but the phenomenon of mode mixing can appear during the application, which results in inaccurate deformation signal extraction.The Ensemble Local Mean Decomposition(ELMD) method can be used to improve the mode of mixing the local mean decomposition by adding auxiliary noise to the original signal, and also can use the statistical characteristics of auxiliary noise to remove the mode mixing. This paper uses simulation data to analyze the model error in the ELMD method and presents a parallel combination prediction method based on ELMD. In the case of high speed railway bridge monitoring data, it divides a series of discrete nonlinear and unstable signal into three product function(PF) components and one remaining component. The method takes advantage of the support vector machine and Kalman filter algorithms to predict these components, and analyses the superiority of ELMD in the case of mode mixing and overall feasibility empirically. The results indicate that: the parallel combination model, based on ensemble local mean decomposition (ELMD), can eliminate the mode mixing problem in the local mean decomposition (LMD) method very well and extracts the deformation signal accurately. In terms of prediction precision, the mean relative error can reach 8.3%, and may provide areference for prediction of deformation monitoring.
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表 1 模型预测精度评定表
Table 1 Comparison of Prediction Errors
期次 ELMD相对误差 LMD相对误差 94 10.069 15.967 95 4.232 6.020 96 4.252 9.919 97 2.755 5.281 98 3.828 6.521 99 8.660 11.131 100 21.497 21.851 101 16.633 18.142 102 8.379 11.569 103 3.361 5.417 MAPE 8.367 11.182 RMSE 0.149 0.213 
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