基于改进EEMD算法的桥梁结构响应信号模态分解研究
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摘要
随着桥梁健康监测系统在桥梁结构上的不断运用,桥梁响应信号预处理的重要性也越发凸显。基于此,分析桥梁响应信号的基本特征,以便选取最佳的信号分解算法-集合经验模态分解;接着分解算法的两弊端:模态混叠现象和有效本征模态函数的筛选;提出了在分解过程中嵌入多元统计学中的"解相关算法"和"谱系聚类"以保证分解所得本征模态函数间满足全局正交性,避免模态混叠现象;同时构建了用于筛选有效本征模态函数的新指标以实现其智能化筛选。该研究分别以仿真信号和实际斜拉桥响应信号为研究对象进行信号的模态分解,并对比分析所得结果。结果表明,所提改进算法能有效改善集合经验模态分解算法存在的问题,同时该算法不仅能运用于仿真信号还能运用于实际桥梁振动信号,且分解结果具有可靠性。
With the continuous application of bridge health monitoring system in bridge structures, the importance of bridge response signal preprocessing is becoming more and more prominent. The basic characteristics of bridge response signals were analyzed, in order to select out the best algorithm for signal decomposition, that is, the ensemble empirical mode decomposition. Considering the two disadvantages of the decomposition algorithm, the phenomenon of modal aliasing and the screening of effective eigenmode functions, a new solution was presented, which embeds the decorrelation algorithm and the pedigree clustering of multivariate statistics analysis in the process of decomposition to ensure the global orthogonality of the intrinsic mode functions, thus effectively avoids the happening of modal mixing. Furthermore, a new index to filter the effective eigenmode function was constructed to realize the intelligent screening. Finally, the modal decomposition of the simulated data and the response signal of an actual cable-stayed bridge were carried out, and all the results were compared and analyzed. The results show that the proposed algorithm can effectively correct the problems of the set empirical mode decomposition algorithm. It can be used not only in simulation signals but also in actual bridge vibration signal, and the decomposition results are reliable.
With the continuous application of bridge health monitoring system in bridge structures, the importance of bridge response signal preprocessing is becoming more and more prominent. The basic characteristics of bridge response signals were analyzed, in order to select out the best algorithm for signal decomposition, that is, the ensemble empirical mode decomposition. Considering the two disadvantages of the decomposition algorithm, the phenomenon of modal aliasing and the screening of effective eigenmode functions, a new solution was presented, which embeds the decorrelation algorithm and the pedigree clustering of multivariate statistics analysis in the process of decomposition to ensure the global orthogonality of the intrinsic mode functions, thus effectively avoids the happening of modal mixing. Furthermore, a new index to filter the effective eigenmode function was constructed to realize the intelligent screening. Finally, the modal decomposition of the simulated data and the response signal of an actual cable-stayed bridge were carried out, and all the results were compared and analyzed. The results show that the proposed algorithm can effectively correct the problems of the set empirical mode decomposition algorithm. It can be used not only in simulation signals but also in actual bridge vibration signal, and the decomposition results are reliable.
引文
[ 1 ] 秦世强.桥梁健康监测与工作模态分析的理论和应用及系统实现[D].成都:西南交通大学,2013.
[ 2 ] 张佳文.基于振动特性的结构损伤识别方法研究[D].长沙:长沙理工大学,2009.
[ 3 ] CAI Yanping,LI Aihua,XU Bing ,et al.The adaptive rule of ensemble empirical mode decomposition with added white noise [J].Journal of Vibration Measurement & Diagnosis,2011,31(6):709-714.
[ 4 ] 单德山,李乔,黄珍.桥梁动力测试信号的自适应分解与重构[J].振动与冲击,2015,34(3):1-6.SHAN Deshan,LI Qiao,HUANG Zhen.Adaptive bridges dynamic test signal decomposition and reconstruction [J].Journal of Vibration and Shock,2015,34(3):1-6.
[ 5 ] 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 London A,1998,454:903-995.
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[ 8 ] 赵玲,刘小峰,秦树人,等.消除经验模态分解中混叠现象的改进掩膜信号法[J].振动与冲击,2010,29( 9) :13-17.ZHAO Ling,LIU Xiaofeng,QIN Shuren,et al.An improved mask signal method for eliminating aliasing in empirical mode decomposition [J].Journal of Vibration and Shock,2010,29 (9):13-17.
[ 9 ] 肖瑛,殷福亮.解相关EMD消除模态混叠的新方法[J].振动与冲击,2015,34(4):25-29.XIAO Ying,YIN Fuliang.Decorrelation EMD:a new method of eliminateing mode mixing [J].Journal of Vibration and Shock,2015,34(4):25-29.
[10] 唐东明.聚类分析及其应用研究[D].成都:电子科技大学,2010.
[11] 林丽,余轮.基于相关系数的EMD改进算法[J].计算与数学工程,2008,36(12):28-29.LIN Li,YU Lun.Improvement on empirical mode decomposition based on correlation coefficient[J].Computer and Digital Engineering,2008,36(12):28-29.
[12] 张雪,龚晓峰.EEMD联合能量熵及小波阈值的语音去噪方法[J].计算机工程与设计,2016,37(3):731-736.ZHANG Xue,GONG Xiaofeng.Method of speech denoising based on EEMD combining energy entropy with wavelet threshold[J].Computer Engineering and Design,2016,37 (3):731-736.
[13] 陈仁祥,汤宝平,马婧华.基于EEMD的振动信号自适应降噪方法[J].振动与冲击,2012,31(15):82-86.CHEN Renxiang,TANG Baoping,MA Jinghua.Adaptive de-noising method based on ensemble empirical mode decomposition for vibration signal [J].Journal of Vibration and Shock ,2012,31(15):82-86.
[14] 周勇军,蔡军哲,石雄伟.基于加权法的桥梁冲击系数计算方法[J].交通运输工程学报,2013,8(4):29-36.ZHOU Yongjun,CAI Junzhe,SHI Xiongwei.Computing method of bridge impact factor based on weighted method[J].Journal of Traffic and Transportation Engineering,2013,8(4):29-36.
[15] 赵绍东.分层加权法在桥梁检测评估中的应用[J].科技创新与应用,2013(11):164-165.ZHAO Shaodong.Application of layered weighting method in bridge inspection and evaluation [J].Technology Innovation and Application,2013(11):164-165.
[16] 林旭泽,王新军,蔡艳平,等.基于AEEMD和峭度-相关系数联合准则的轴承故障诊断[J].轴承,2015(8):55-58.LIN Xuze,WANG Xinjun,CAI Yangping,et al.Fault diagonosis for bearings based on AEEMD and kurtosis-correlation coefficients joint criterion[J].Bearing,2015(8):55-58.
[17] 陈仁祥,汤宝平.基于相关系数的EEMD转子振动信号降噪方法[J].振动、测试与诊断,2012,32(4):542-546.CHEN Renxiang,TANG Baoping.Ensemble empirical mode decomposition de-noising method based on correlation coefficients for vibration signal of rotor system [J].Journal of Vibration Measurement & Diagnosis,2012,32(4):542-546.
[ 2 ] 张佳文.基于振动特性的结构损伤识别方法研究[D].长沙:长沙理工大学,2009.
[ 3 ] CAI Yanping,LI Aihua,XU Bing ,et al.The adaptive rule of ensemble empirical mode decomposition with added white noise [J].Journal of Vibration Measurement & Diagnosis,2011,31(6):709-714.
[ 4 ] 单德山,李乔,黄珍.桥梁动力测试信号的自适应分解与重构[J].振动与冲击,2015,34(3):1-6.SHAN Deshan,LI Qiao,HUANG Zhen.Adaptive bridges dynamic test signal decomposition and reconstruction [J].Journal of Vibration and Shock,2015,34(3):1-6.
[ 5 ] 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 London A,1998,454:903-995.
[ 6 ] 严鹏.基于信号理论的桥梁健康监测降噪处理和损伤识别研究[D].成都:西南交通大学,2011.
[ 7 ] 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):41.
[ 8 ] 赵玲,刘小峰,秦树人,等.消除经验模态分解中混叠现象的改进掩膜信号法[J].振动与冲击,2010,29( 9) :13-17.ZHAO Ling,LIU Xiaofeng,QIN Shuren,et al.An improved mask signal method for eliminating aliasing in empirical mode decomposition [J].Journal of Vibration and Shock,2010,29 (9):13-17.
[ 9 ] 肖瑛,殷福亮.解相关EMD消除模态混叠的新方法[J].振动与冲击,2015,34(4):25-29.XIAO Ying,YIN Fuliang.Decorrelation EMD:a new method of eliminateing mode mixing [J].Journal of Vibration and Shock,2015,34(4):25-29.
[10] 唐东明.聚类分析及其应用研究[D].成都:电子科技大学,2010.
[11] 林丽,余轮.基于相关系数的EMD改进算法[J].计算与数学工程,2008,36(12):28-29.LIN Li,YU Lun.Improvement on empirical mode decomposition based on correlation coefficient[J].Computer and Digital Engineering,2008,36(12):28-29.
[12] 张雪,龚晓峰.EEMD联合能量熵及小波阈值的语音去噪方法[J].计算机工程与设计,2016,37(3):731-736.ZHANG Xue,GONG Xiaofeng.Method of speech denoising based on EEMD combining energy entropy with wavelet threshold[J].Computer Engineering and Design,2016,37 (3):731-736.
[13] 陈仁祥,汤宝平,马婧华.基于EEMD的振动信号自适应降噪方法[J].振动与冲击,2012,31(15):82-86.CHEN Renxiang,TANG Baoping,MA Jinghua.Adaptive de-noising method based on ensemble empirical mode decomposition for vibration signal [J].Journal of Vibration and Shock ,2012,31(15):82-86.
[14] 周勇军,蔡军哲,石雄伟.基于加权法的桥梁冲击系数计算方法[J].交通运输工程学报,2013,8(4):29-36.ZHOU Yongjun,CAI Junzhe,SHI Xiongwei.Computing method of bridge impact factor based on weighted method[J].Journal of Traffic and Transportation Engineering,2013,8(4):29-36.
[15] 赵绍东.分层加权法在桥梁检测评估中的应用[J].科技创新与应用,2013(11):164-165.ZHAO Shaodong.Application of layered weighting method in bridge inspection and evaluation [J].Technology Innovation and Application,2013(11):164-165.
[16] 林旭泽,王新军,蔡艳平,等.基于AEEMD和峭度-相关系数联合准则的轴承故障诊断[J].轴承,2015(8):55-58.LIN Xuze,WANG Xinjun,CAI Yangping,et al.Fault diagonosis for bearings based on AEEMD and kurtosis-correlation coefficients joint criterion[J].Bearing,2015(8):55-58.
[17] 陈仁祥,汤宝平.基于相关系数的EEMD转子振动信号降噪方法[J].振动、测试与诊断,2012,32(4):542-546.CHEN Renxiang,TANG Baoping.Ensemble empirical mode decomposition de-noising method based on correlation coefficients for vibration signal of rotor system [J].Journal of Vibration Measurement & Diagnosis,2012,32(4):542-546.