基于序列相关和小波变换的加速度计信号降噪
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摘要
有效滤除加速度计信号噪声,对提高加速度计信号分析精度有重要作用。针对加速度计比较法冲击激励校准中,噪声对加速度计频率响应函数估计的影响,提出一种基于序列相关和小波变换的加速度计信号降噪方法。该方法首先对参考加速度计和被校加速度计的输出信号进行小波变换,然后利用同一激励下不同加速度计响应信号小波系数的相关性,引入互相关系数对阈值进行改进,并应用该阈值对加速度计输出信号各层小波系数进行降噪。仿真实验和加速度计比较法冲击激励校准实验表明,该方法能够有效降低加速度计输出信号中的噪声,提高了加速度计频率响应函数估计精度。
Effective filtering accelerometer signal noise has an important effect on improving the accuracy of accelerometer signal analysis. Aimed at the influence of noise on the estimation of the frequency response function of accelerometers in the accelerometer comparison method impact excitation calibration,an accelerometer signal de-noising method based on sequence correlation and wavelet transform is presented. In this method,the wavelet transform is applied on the output signals of the reference accelerometer and the calibrated accelerometer. Then,according to the correlation of the wavelet coefficients of the response signals of different accelerometers under the same excitation,the cross correlation coefficient is introduced into improving the threshold,and the wavelet coefficients of each layer of the accelerometer output signals are de-noising with the threshold. Simulation experiment and accelerometer comparison method of shock excitation calibration experiment show that the presented method can effectively reduce the noise in the output signal of the accelerometer and improve the estimation accuracy of the frequency response function of accelerometers.
Effective filtering accelerometer signal noise has an important effect on improving the accuracy of accelerometer signal analysis. Aimed at the influence of noise on the estimation of the frequency response function of accelerometers in the accelerometer comparison method impact excitation calibration,an accelerometer signal de-noising method based on sequence correlation and wavelet transform is presented. In this method,the wavelet transform is applied on the output signals of the reference accelerometer and the calibrated accelerometer. Then,according to the correlation of the wavelet coefficients of the response signals of different accelerometers under the same excitation,the cross correlation coefficient is introduced into improving the threshold,and the wavelet coefficients of each layer of the accelerometer output signals are de-noising with the threshold. Simulation experiment and accelerometer comparison method of shock excitation calibration experiment show that the presented method can effectively reduce the noise in the output signal of the accelerometer and improve the estimation accuracy of the frequency response function of accelerometers.
引文
[1]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].Proc Rsoc Lond,1998,454:56-78.
[2]刘霞,黄阳,黄敬,等.基于经验模态分解(EMD)的小波熵阈值地震信号去噪[J].吉林大学学报(地),2016,46(1):262-269.
[3]李其建,徐海波.改进EMD阈值小波滤波方法[J].机械科学与技术,2017,36(8):1175-1179.
[4]崔冰波,陈熙源,宋锐.EMD阈值滤波在光纤陀螺漂移信号去噪中的应用[J].光学学报,2015,35(2):53-58.
[5]Mohguen W,Ra6s El’hadiBekka.New Denoising Method Based on Empirical Mode Decomposition and Improved Thresholding Function[C]//Journal of Physics Conference Series.Journal of Physics Conference Series,2017:012-014.
[6]Farge M.Wavelet Transform and Their Application to Turbulence[J].Annu.rev.fluidMech,1992,56(4):68-68.
[7]Callaway D S,Newman M E J,Strogatz S H,et al.Network Robustness and Fragility:Percolation on Random Graphs[J].Physical Review Letters,2000,85(25):5468.
[8]李红延,周云龙,田峰,等.一种新的小波自适应阈值函数振动信号去噪算法[J].仪器仪表学报,2015,36(10):2200-2206.
[9]苏立,南海鹏,余向阳,等.基于改进阈值函数的小波降噪分析在水电机组振动信号中的应用[J].水力发电学报,2012,31(3):246-251.
[10]秦毅,王家序,毛永芳.基于软阈值和小波模极大值重构的信号降噪[J].振动、测试与诊断,2011,31(5):543-547.
[11]贾毅婷,张东来,张斌.基于小波尺度相关性的暂态数据降噪压缩方法[J].电力系统自动化,2013,37(5):68-73.
[12]Donoho D L.De-Noising by Soft-Thresholding[J].IEEE Transactions on Information Theory,2002,41(3):613-627.
[13]王维,张英堂,任国全.小波阈值降噪算法中最优分解层数的自适应确定及仿真[J].仪器仪表学报,2009,30(3):526-530.
[14]蔡铁,朱杰.小波阈值降噪算法中最优分解层数的自适应选择[J].控制与决策,2006,21(2):217-220.
[15]Liu H,Wang W,Xiang C,et al.A De-Noising Method Using the Improved Wavelet Threshold Function Based on Noise Variance Estimation[J].Mechanical Systems&Signal Processing,2018,99:30-46.
[16]李昕,王惠惠,王月茹,等.基于能量估计的小波阈值与经验模态分解相结合的滤波方法研究[J].生物医学工程学杂志,2011(6):1098-1102.
[17]邵忍平,曹精明,李永龙.基于EMD小波阈值去噪和时频分析的齿轮故障模式识别与诊断[J].振动与冲击,2012,31(8):96-101.
[18]周涛涛,朱显明,彭伟才,等.基于CEEMD和排列熵的故障数据小波阈值降噪方法[J].振动与冲击,2015,34(23):207-211.
[19]董文永,丁红,董学士,等.一种小波自适应阈值全频降噪方法[J].电子学报,2015,43(12):2374-2380.
[20]马增强,谷朝健,王建东.基于互相关函数的滚动轴承故障诊断方法研究[J].应用基础与工程科学学报,2017(3):646-655.
[21]杜必强,孙立江.基于EMD和小波熵阈值算法的超声回波信号降噪[J].中国测试,2017,43(1):101-105.
[22]刘书俊,李生林,蒋明,等.一种基于加权平均的改进型小波阈值降噪算法[J].化工自动化及仪表,2017,44(3):239-242.
[23]陶珂,朱建军.多指标融合的小波去噪最佳分解尺度选择方法[J].测绘学报,2012,41(5):749-755.
[2]刘霞,黄阳,黄敬,等.基于经验模态分解(EMD)的小波熵阈值地震信号去噪[J].吉林大学学报(地),2016,46(1):262-269.
[3]李其建,徐海波.改进EMD阈值小波滤波方法[J].机械科学与技术,2017,36(8):1175-1179.
[4]崔冰波,陈熙源,宋锐.EMD阈值滤波在光纤陀螺漂移信号去噪中的应用[J].光学学报,2015,35(2):53-58.
[5]Mohguen W,Ra6s El’hadiBekka.New Denoising Method Based on Empirical Mode Decomposition and Improved Thresholding Function[C]//Journal of Physics Conference Series.Journal of Physics Conference Series,2017:012-014.
[6]Farge M.Wavelet Transform and Their Application to Turbulence[J].Annu.rev.fluidMech,1992,56(4):68-68.
[7]Callaway D S,Newman M E J,Strogatz S H,et al.Network Robustness and Fragility:Percolation on Random Graphs[J].Physical Review Letters,2000,85(25):5468.
[8]李红延,周云龙,田峰,等.一种新的小波自适应阈值函数振动信号去噪算法[J].仪器仪表学报,2015,36(10):2200-2206.
[9]苏立,南海鹏,余向阳,等.基于改进阈值函数的小波降噪分析在水电机组振动信号中的应用[J].水力发电学报,2012,31(3):246-251.
[10]秦毅,王家序,毛永芳.基于软阈值和小波模极大值重构的信号降噪[J].振动、测试与诊断,2011,31(5):543-547.
[11]贾毅婷,张东来,张斌.基于小波尺度相关性的暂态数据降噪压缩方法[J].电力系统自动化,2013,37(5):68-73.
[12]Donoho D L.De-Noising by Soft-Thresholding[J].IEEE Transactions on Information Theory,2002,41(3):613-627.
[13]王维,张英堂,任国全.小波阈值降噪算法中最优分解层数的自适应确定及仿真[J].仪器仪表学报,2009,30(3):526-530.
[14]蔡铁,朱杰.小波阈值降噪算法中最优分解层数的自适应选择[J].控制与决策,2006,21(2):217-220.
[15]Liu H,Wang W,Xiang C,et al.A De-Noising Method Using the Improved Wavelet Threshold Function Based on Noise Variance Estimation[J].Mechanical Systems&Signal Processing,2018,99:30-46.
[16]李昕,王惠惠,王月茹,等.基于能量估计的小波阈值与经验模态分解相结合的滤波方法研究[J].生物医学工程学杂志,2011(6):1098-1102.
[17]邵忍平,曹精明,李永龙.基于EMD小波阈值去噪和时频分析的齿轮故障模式识别与诊断[J].振动与冲击,2012,31(8):96-101.
[18]周涛涛,朱显明,彭伟才,等.基于CEEMD和排列熵的故障数据小波阈值降噪方法[J].振动与冲击,2015,34(23):207-211.
[19]董文永,丁红,董学士,等.一种小波自适应阈值全频降噪方法[J].电子学报,2015,43(12):2374-2380.
[20]马增强,谷朝健,王建东.基于互相关函数的滚动轴承故障诊断方法研究[J].应用基础与工程科学学报,2017(3):646-655.
[21]杜必强,孙立江.基于EMD和小波熵阈值算法的超声回波信号降噪[J].中国测试,2017,43(1):101-105.
[22]刘书俊,李生林,蒋明,等.一种基于加权平均的改进型小波阈值降噪算法[J].化工自动化及仪表,2017,44(3):239-242.
[23]陶珂,朱建军.多指标融合的小波去噪最佳分解尺度选择方法[J].测绘学报,2012,41(5):749-755.
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