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ELMD并联式组合模型在沉降分析中的可行性研究
引用本文:吴开岩,张献州,黄雨微,杨龙杰,马龙,吴战广,王鹏.ELMD并联式组合模型在沉降分析中的可行性研究[J].武汉大学学报(信息科学版),2017,42(10):1482-1488.
作者姓名:吴开岩  张献州  黄雨微  杨龙杰  马龙  吴战广  王鹏
作者单位:1.四川省第三测绘工程院, 四川 成都, 610500
基金项目:国家自然科学基金41374002四川省科技计划项目2015JQ0046长江学者和创新团队发展计划项目IRT13092
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。

关 键 词:精密工程测量    总体局部均值分解    模态混叠    非线性预测    沉降监测
收稿时间:2017-04-11

The Feasibility Study on Settlement Monitoring of a Parallel Combination Prediction Method Based on ELMD
Institution:1.The Third Academy of Engineering of Surveying and Mapping, Chengdu 610500, China2.Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 610031, China3.Sichuang Tunnel Tang Technology Co., Ltd, Chengdu 610031, China4.Zhuzhou CRRC Times Electric Co., Ltd, Zhuzhou 412001, China
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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