振动信号Burg谱估计算法的性能优化研究
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摘要
Burg算法是振动信号功率谱估计中常用的方法,具有分辨力性能优良、计算效率高的优点。但当数据为实数样本时会出现谱线分裂、谱峰偏移、信号初始相位和预测滤波器阶数对结果影响大等问题,影响了振动信号谱估计分析的准确性。针对Burg算法存在的问题,提出将最优窗引入到Burg算法中,通过信息论准则确定预测滤波器的最优阶次,通过加最优窗函数改善谱线分裂和谱峰偏移现象,实现对Burg谱估计算法的优化。结合工业现场实测汽轮机振动信号,利用仿真工具进行实验分析和仿真,并与直接Burg算法进行了仿真对比。理论分析和实验结果表明,最优阶次下的加窗Burg算法中谱线分裂、谱峰偏移现象得到明显改善,具有更优良的分辨力性能,提高了振动信号谱估计的准确性和质量。
With the advantages of high resolution and efficiency of calculation,Burg algorithm is a commonly used method in vibration signal power spectral estimation.Aimed at the problems in Burg algorithm,this paper introduces optimal window into Burg algorithm,and determines the optimal order by AIC.The optimization of the Burg spectral estimation algorithm was realized by using the optimal window function to improve the spectral line splitting and peak shifting.Combing with the turbine's vibration signals which collected industrial site and the simulation tools to analyze and simulate the experiment,the simulation results were compare with the original Burg algorithm.Theoretical analysis and experimental results show that the spectral line splitting and peak shifting in windowed Burg algorithm under the optimal order are improved obviously,which has better resolution performance and improves the accuracy and quality of the vibration signal spectral estimation.
With the advantages of high resolution and efficiency of calculation,Burg algorithm is a commonly used method in vibration signal power spectral estimation.Aimed at the problems in Burg algorithm,this paper introduces optimal window into Burg algorithm,and determines the optimal order by AIC.The optimization of the Burg spectral estimation algorithm was realized by using the optimal window function to improve the spectral line splitting and peak shifting.Combing with the turbine's vibration signals which collected industrial site and the simulation tools to analyze and simulate the experiment,the simulation results were compare with the original Burg algorithm.Theoretical analysis and experimental results show that the spectral line splitting and peak shifting in windowed Burg algorithm under the optimal order are improved obviously,which has better resolution performance and improves the accuracy and quality of the vibration signal spectral estimation.
引文
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[5]张峰,张馨月,马舒啸,石现峰.汽轮机振动信号的自回归模型谱估计算法[J].西安工业大学学报,2013-12:1004-1008.
[6]才建南.基于BURG法无线电频谱信号的高分辨率估计研究[D].兰州交通大学,2014.
[7]闫庆华,程兆刚,段云龙.AR模型功率谱估计及Matlab实现[J].计算机与数字工程,2010-4:154-156.
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[13]陈国志,张健,丛贇,卢志飞,汪洋.基于最优加权Burg谱估计的间谐波智能分析方法[J].电工电能新技术,2014-1:26-29.46.
[14]李明,王晓茹.基于最优窗burg算法的电力系统间谐波谱分析[J].电工技术学报,2011,26(1):178-181.
[15]支冬栋,卫红凯,杜斌.现代谱估计法中几种不同模型参数估计法的比较[J].舰船电子对抗,2009-1:100-102.
[2]白江飞.现代信号处理方法及其在发动机振动信号分析中的应用[D].西北工业大学,2004.
[3]邓泽怀,刘波波,李彦良.常见的功率谱估计方法及其Matlab仿真[J].电子科技,2014-2:50-52.
[4]姚文俊.自相关法和Burg法在AR模型功率谱估计中的仿真研究[J].计算机与数字工程,2007-10:32-34,187.
[5]张峰,张馨月,马舒啸,石现峰.汽轮机振动信号的自回归模型谱估计算法[J].西安工业大学学报,2013-12:1004-1008.
[6]才建南.基于BURG法无线电频谱信号的高分辨率估计研究[D].兰州交通大学,2014.
[7]闫庆华,程兆刚,段云龙.AR模型功率谱估计及Matlab实现[J].计算机与数字工程,2010-4:154-156.
[8]D N Swingler.A modified Burg algorithm for maximum entropy spectral analysis[C].Proc.IEEE,1979,67(9):1368-1369.
[9]M Kaveh,G Lippert.An optimum tapered Burg algorithm for linear prediction and spectral analysis[J].IEEE Transactions on Acoustics,Speech.
[10]Marple.L,A new autoregressive spectrum analysis algorithm[J].IEEE Transactions on Acoustics,Speech,and Signal Processing,1980,ASSP,28(4):441-454.
[11]M Wax,T Kailath.Detection of signals by information theoretic criteria[J].IEEE Trans,on ASSP.1985,33(2):387-392.
[12]S M Kay,S L Marple.Spectrum analysis:a modern perspective[J].Proc IEEE 1981,69:1380-419.
[13]陈国志,张健,丛贇,卢志飞,汪洋.基于最优加权Burg谱估计的间谐波智能分析方法[J].电工电能新技术,2014-1:26-29.46.
[14]李明,王晓茹.基于最优窗burg算法的电力系统间谐波谱分析[J].电工技术学报,2011,26(1):178-181.
[15]支冬栋,卫红凯,杜斌.现代谱估计法中几种不同模型参数估计法的比较[J].舰船电子对抗,2009-1:100-102.