Denoising Method for Deformation Monitoring Data Based on ICEEMD-ICA and MDP Principle
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摘要: 针对经验模态分解（empirical mode decomposition，EMD）方法存在信噪分离不准确的缺陷，以及独立分量分析（independent component analysis，ICA）存在不确定性的问题，提出了一种改进完备集成经验模态分解（improved complete ensemble empirical mode decomposition, ICEEMD）、ICA与最小失真准则（minimal distortion principle，MDP）相结合进行变形数据去噪的方法。首先，使用ICEEMD方法对变形监测数据进行有效分解，并以此构建虚拟噪声信号；其次，对虚拟噪声进行二次ICEEMD分解，提取更接近真实噪声的二次虚拟噪声信号，再以二次虚拟噪声和原变形数据组成输入观测通道，使用ICA进行处理；然后，通过计算ICA处理后的独立分量与输入信号的相关系数，解决独立分量的排序不确定性与相位不确定性问题；最后，使用MDP准则有效解决了独立分量的幅值不确定性。对加噪仿真数据和实际桥梁GNSS变形监测数据进行详细分析，结果表明，所提方法可取得良好的去噪效果，有效提升去噪的性能指标，充分验证了所提方法在变形监测数据去噪中具备的可行性和有效性。
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关键词:
- 改进完备集成经验模态分解 /
- 独立分量分析 /
- 二次虚拟噪声 /
- 最小失真准则 /
- 变形监测数据去噪
Abstract:Objectives Considering the inaccurate separation of signal and noise of empirical mode decomposition (EMD) method and the uncertainty of independent component analysis (ICA), a new method for denoising deformation data with improved complete ensemble empirical mode decomposition (ICEEMD), independent component analysis (ICA) and minimal distortion principle (MDP) is proposed.Methods Firstly, ICEEMD method is used to decompose the deformation monitoring data effectively, and the virtual noise signal is constructed. Secondly, ICEEMD decomposition of virtual noise is carried out to extract twice virtual noise signal which is closer to real noise. The input observation channel is composed of twice virtual noise and original deformation data and processed by ICA. Then, by calculating the correlation coefficient between the independent components and the input signal after ICA processing, the sorting uncertainty and phase uncertainty of independent components can be solved. Finally, the MDP criterion is used to effectively solve the amplitude uncertainty of independent components.Results Through the detailed analysis of noisy simulation data and actual bridge GNSS deformation monitoring data, the results show that the proposed method has achieved good denoising effect and can effectively improve the performance of denoising.Conclusions It also fully verified the feasibility and effectiveness of the proposed method indenoising of deformation monitoring data. -
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图 2 各个IMF分量与原加噪Bumps信号的相关系数
Figure 2. Correlation Coefficient of Each IMF and Original Noisy Bumps Signal
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图 3 二次分解后各个IMF分量与noise信号的相关系数
Figure 3. Correlation Coefficient Between IMF Components and Noise Signal After Two Decomposition
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图 4 noise2信号及noise信号中的有效成分
Figure 4. Noise2 Signal and Effective Components in Noise Signal
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图 10 实例数据中消除不确定性后ICA分离结果
Figure 10. ICA Separation Result After Eliminating Uncertainties of Instance Data
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图 11 实例数据中4种方法的去噪结果对比
Figure 11. Comparison of Denoising Results of Instance Data Using Four Methods
表 1 4种方法的去噪指标
Table 1 Denoising Indexes of Four Methods
去噪方法 SNR/dB RMSE ICEEMD 10.727 3 0.209 8 EMD-ICA 9.514 7 0.241 3 ICEEMD-ICA 10.152 1 0.224 2 本文方法 12.765 7 0.165 9 
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表 2 实例数据中4种方法的去噪指标
Table 2 Denoising Indexes of Four Methods in Instance Data
去噪方法 SNR/dB RMSE/ m ICEEMD 24.964 2 7.768 8 EMD-ICA 23.263 4 9.449 1 ICEEMD-ICA 25.518 0 7.288 9 本文方法 29.129 8 4.809 2
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[1] 章浙涛, 朱建军, 匡翠林, 等. 一种小波包混合滤波方法及其应用[J]. 武汉大学学报·信息科学版, 2014, 39(4): 471-475 doi: 10.13203/j.whugis20120141 Zhang Zhetao, Zhu Jianjun, Kuang Cuilin, et al. A Hybrid Filter Method Based on Wavelet Packet and Its Application[J]. Geomatics and Information Science of Wuhan University, 2014, 39(4): 471- 475 doi: 10.13203/j.whugis20120141
[2] 栾元重, 栾亨宣, 李伟, 等. 桥梁变形监测数据小波去噪与Kalman滤波研究[J]. 大地测量与地球动力学, 2015, 35(6): 1 041-1 045 https://www.cnki.com.cn/Article/CJFDTOTAL-DKXB201506031.htm Luan Yuanzhong, Luan Hengxuan, Li Wei, et al. Research on Wavelet Denoising and Kalman Filter in Bridge Deformation Data[J]. Journal of Geodesy and Geodynamics, 2015, 35(6): 1 041-1 045 https://www.cnki.com.cn/Article/CJFDTOTAL-DKXB201506031.htm
[3] Yi T H, Li H N, Gu M. Wavelet Based Multi-step Filtering Method for Bridge Health Monitoring Using GPS and Accelerometer[J]. Smart Structures and Systems, 2013, 11(4): 331-348 doi: 10.12989/sss.2013.11.4.331
[4] Huang N E, Shen Z, Long S R, et al. The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlinear and Non-stationary Time Series Analysis[J]. Proceeding of the Royal Society: A Mathematical Physical & Engineering Sciences, 1998, 454 (1): 903-995 http://www.ccpo.odu.edu/~klinck/Reprints/PDF/huangPRSLA1998.pdf
[5] 罗亦泳, 黄城, 张静影. 基于变分模态分解的变形监测数据去噪方法[J]. 武汉大学学报·信息科学版, 2020, 45(5): 784-790 doi: 10.13203/j.whugis20180437 Luo Yiyong, Huang Cheng, Zhang Jingying. Denoising Method of Deformation Monitoring Data Based on Variational Mode Decomposition[J]. Geomatics and Information Science of Wuhan University, 2020, 45(5): 784-790 doi: 10.13203/j.whugis20180437
[6] 范千. 单历元GPS变形信息特征提取的EMD方法[J]. 山东科技大学学报(自然科学版), 2011, 30(5): 78-82 https://www.cnki.com.cn/Article/CJFDTOTAL-SDKY201105016.htm Fan Qian. Empirical Mode Decomposition Method for Single Epoch GPS Deformation Information Characteristic Extracting[J]. Journal of Shandong University of Science and Technology, 2011, 30 (5): 78-82 https://www.cnki.com.cn/Article/CJFDTOTAL-SDKY201105016.htm
[7] 罗飞雪, 戴吾蛟. 小波分解与EMD在变形监测应用中的比较[J]. 大地测量与地球动力学, 2010, 30(3): 137-141 https://www.cnki.com.cn/Article/CJFDTOTAL-DKXB201003034.htm Luo Feixue, Dai Wujiao. Comparison of EMD with Wavelet Decomposition for Dynamic Deformation Monitoring[J]. Journal of Geodesy and Geodynamics, 2010, 30(3): 137-141 https://www.cnki.com.cn/Article/CJFDTOTAL-DKXB201003034.htm
[8] Wu Z H, Huang N E. Ensemble Empirical Mode Decomposition: A Noise Assisted Data Analysis Method[J]. Advances in Adaptive Data Analysis, 2009, 1 (1): 1-41 doi: 10.1142/S1793536909000047
[9] Torres M E, Colominas M A, Schlotthauer G, et al. A Complete Ensemble Empirical Mode Decom position with Adaptive Noise[C]// International Conference on Acoustics, Speech and Signal Processing(ICASSP), Prague, Czech Republic, 2011
[10] Colominas M A, Schlotthauer G, Torres M E. Improved Complete Ensemble EMD: A Suitable Tool for Biomedical Signal Processing[J]. Biomedical Signal Processing and Control, 2014, 14(1): 19-29 http://download.xuebalib.com/n2fU9SncaWt.pdf
[11] 吴金斌, 周世健. 经验模式分解联合独立分量分析降噪研究[J]. 测绘科学, 2016, 41(7): 197-201 https://www.cnki.com.cn/Article/CJFDTOTAL-CHKD201607035.htm Wu Jinbin, Zhou Shijian. The De-noising Study Based on Empirical Mode Decomposition and Independent Component Analysis[J]. Science of Surveying and Mapping, 2016, 41(7): 197-201 https://www.cnki.com.cn/Article/CJFDTOTAL-CHKD201607035.htm
[12] 于帅, 刘超, 李盟盟, 等. EMD-WaveletICA耦合模型及其在GPS坐标序列降噪中的应用[J]. 测绘科学技术学报, 2016, 33(2): 139-144 https://www.cnki.com.cn/Article/CJFDTOTAL-JFJC201602006.htm Yu Shuai, Liu Chao, Li Mengmeng, et al. EMDWavelet-ICA Coupled Model and Its Application in GPS Coordinate Series De-noising[J]. Journal of Geomatics Science and Technology, 2016, 33(2): 139-144 https://www.cnki.com.cn/Article/CJFDTOTAL-JFJC201602006.htm
[13] 赫彬, 张雅婷, 白艳萍. 基于ICA-CEEMD小波阈值的传感器信号去噪[J]. 振动与冲击, 2017, 36(4): 226-232 https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ201704036.htm He Bin, Zhang Yating, Bai Yanping. A Method for Sensor Signal De-noising Based on ICA-CEEMD Wavelet Threshold[J]. Journal of Vibration and Shock, 2017, 36(4): 226-232 https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ201704036.htm
[14] 王胜, 李磊. 加权优选方法在信号分解中的应用与软件研制[J]. 测绘地理信息, 2020, 45(5): 54-58 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXG202005013.htm Wang Sheng, Li Lei. Application and Software Development of Weighted Optimization Method Used on Signal Decomposition[J]. Journal of Geomatics, 2020, 45(5): 54-58 https://www.cnki.com.cn/Article/CJFDTOTAL-CHXG202005013.htm
[15] 徐信, 李志华, 杨越. 基于EMD-ICA的激电数据降噪处理方法[J]. 计算机应用研究, 2017, 34(6): 1 737-1 741 https://www.cnki.com.cn/Article/CJFDTOTAL-JSYJ201706031.htm Xu Xin, Li Zhihua, Yang Yue. De-noising of IP Data Based on EMD-ICA[J]. Application Research of Computers, 2017, 34(6): 1 737-1 741 https://www.cnki.com.cn/Article/CJFDTOTAL-JSYJ201706031.htm
[16] 谢将剑, 张军国, 董方舟, 等. 基于虚拟观测的FastICA的坠砣位移的风荷载分量去除[J]. 铁道学报, 2014, 36(7): 44-50 https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB201407008.htm Xie Jiangjian, Zhang Junguo, Dong Fangzhou, et al. FastICA Remove of Windload Component from Balance Weight Displacement Based on Virtual Observation[J]. Jouranl of the China Railway Society, 2014, 36(7): 44-50 https://www.cnki.com.cn/Article/CJFDTOTAL-TDXB201407008.htm
[17] Hyvarinen A. Fast and Robust Fixed-Point Algorithms for Independent Component Analysis[J]. IEEE Transactions on Neural Networks, 1999, 10 (3): 626-634 http://www.cs.helsinki.fi/u/ahyvarin/papers/TNN99_reprint.pdf
[18] 刘东瀛, 邓艾东, 刘振元, 等. 基于EMD与相关系数原理的故障声发射信号降噪研究[J]. 振动与冲击, 2017, 36(19): 71-77 https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ201719011.htm Liu Dongying, Deng Aidong, Liu Zhenyuan, et al. De-noising Method for Fault Acoustic Emission Signals Based on the EMD and Correlation Coefficient[J]. Journal of Vibration and Shock, 2017, 36 (19): 71-77 https://www.cnki.com.cn/Article/CJFDTOTAL-ZDCJ201719011.htm
[19] Matsuoka K. Minimal Distortion Principle for Blind Source Separation[C]// The 41st SICE Annual Conference, New York, USA, 2002
[20] Meng X L, Nguyen D T, Xie Y L, et al. Design and Implementation of a New System for Large Bridge Monitoring-GeoSHM[J]. Sensors, 2018, 18(1): 775-797 http://www.mdpi.com/1424-8220/18/3/775/pdf
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