一种高时频聚集性的方法在非平稳信号分析中的应用
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摘要
非平稳信号广泛存在于自然界及工程实践中,时频分析是处理非平稳信号的有力工具。时频聚集性是评价时频分析方法的重要指标,传统时频分析方法在时频聚集性上已经不能满足要求,同步压缩小波变换将小波系数在频率方向进行压缩,能够有效提高时频聚集性。本文将此方法分别用于不同信噪比下的单分量及多分量信号分析,并与传统方法对比。结果表明该方法具有较强的噪声鲁棒性,对于复杂多分量信号仍能保持高时频聚集性。最后用于变转速滚动轴承故障信号分析,进一步验证了此方法的实用性。
Non-stationary signal is widely used in nature and engineering practice. Time-frequency analysis is a powerful tool for dealing with non-stationary signals. Time-frequency concentration is an important indicator to evaluate the time-frequency analysis method, while the traditional time-frequency analysis method cannot meet the requirements on the time-frequency concentration. The synchrosqueezing wavelet transform is a new time-frequency analysis method by compressing the wavelet coefficients in frequency direction and can effectively improve the time-frequency concentration. In this paper, this method is used for single component and multi-component signals under different signal-to-ratio, and compared with the traditional method. The results show that the method has stronger noise robustness and can keep high concentration for complex multi-component signal. At last, the method is used to analyze the fault signal of rolling bearing under varying speed, which further verify the practicability of this method.
Non-stationary signal is widely used in nature and engineering practice. Time-frequency analysis is a powerful tool for dealing with non-stationary signals. Time-frequency concentration is an important indicator to evaluate the time-frequency analysis method, while the traditional time-frequency analysis method cannot meet the requirements on the time-frequency concentration. The synchrosqueezing wavelet transform is a new time-frequency analysis method by compressing the wavelet coefficients in frequency direction and can effectively improve the time-frequency concentration. In this paper, this method is used for single component and multi-component signals under different signal-to-ratio, and compared with the traditional method. The results show that the method has stronger noise robustness and can keep high concentration for complex multi-component signal. At last, the method is used to analyze the fault signal of rolling bearing under varying speed, which further verify the practicability of this method.
引文
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[2]Daubechies I,Lu F J,Wu H T.Synchrosqueezed wavelet transforms:An empirical mode decomposition-like tool[J].Appl.Comput.Harmon.Anal.,2011,30(2):243-261.
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[4]Feng Z P,Chen X W,Liang M.Iterative generalized synchrosqueezing transform for fault diagnosis of wind turbine planetary gearbox under non-stationary conditions[J].Mechanical Systems and Signal Processing,2015,52:360-375.
[5]Wang S B,Chen X F,Selesnick I W,et al.Mat55ching synchrosqueezing transform:A useful tool for characterizing signals with fast varying instantaneous frequency and application to machine fault diagnosis[J].Mechanical Systems and Signal Processing,2018,100:242-288.
[6]刘景良,任伟新,王佐才,等.基于同步挤压小波变换的结构瞬时频率识别[J].振动与冲击,2013,32(18):37-42.
[7]刘景良,郑锦仰,郑文婷,等.基于改进同步挤压小波变换识别信号瞬时频率[J].振动、测试与诊断,2017,37(4):814-821.
[8]汪祥莉,王斌,王文波,等.混沌干扰中基于同步挤压小波变换的谐波信号提取方法[J].物理学报,2015,64(10):100201-1-100201-8.
[9]金艳,高舵,姬红兵.复杂噪声下基于同步压缩Chirplet变换的LFM信号参数估计[J].电子与信息学报,2017,39(8):1906-1911.
[10]于刚.挖掘机振声信号时频分析研究与应用[D].济南:山东大学,2016.
[11]Iatsenko D,Mcclintock P V E,Stefanovska A.Linear and synchrosqueezed time-frequency representations revisited:Overview,standards of use,resolution,reconstruction,concentration,and algorithms[J].Digital Signal Processing,2015,42:1-26.
[2]Daubechies I,Lu F J,Wu H T.Synchrosqueezed wavelet transforms:An empirical mode decomposition-like tool[J].Appl.Comput.Harmon.Anal.,2011,30(2):243-261.
[3]Chuan L,Liang M.Time-frequency signal analysis for gearbox fault diagnosis using a generalized synchrosqueezing transform[J].Mechanical Systems and Signal Processing,2012,26:205-217.
[4]Feng Z P,Chen X W,Liang M.Iterative generalized synchrosqueezing transform for fault diagnosis of wind turbine planetary gearbox under non-stationary conditions[J].Mechanical Systems and Signal Processing,2015,52:360-375.
[5]Wang S B,Chen X F,Selesnick I W,et al.Mat55ching synchrosqueezing transform:A useful tool for characterizing signals with fast varying instantaneous frequency and application to machine fault diagnosis[J].Mechanical Systems and Signal Processing,2018,100:242-288.
[6]刘景良,任伟新,王佐才,等.基于同步挤压小波变换的结构瞬时频率识别[J].振动与冲击,2013,32(18):37-42.
[7]刘景良,郑锦仰,郑文婷,等.基于改进同步挤压小波变换识别信号瞬时频率[J].振动、测试与诊断,2017,37(4):814-821.
[8]汪祥莉,王斌,王文波,等.混沌干扰中基于同步挤压小波变换的谐波信号提取方法[J].物理学报,2015,64(10):100201-1-100201-8.
[9]金艳,高舵,姬红兵.复杂噪声下基于同步压缩Chirplet变换的LFM信号参数估计[J].电子与信息学报,2017,39(8):1906-1911.
[10]于刚.挖掘机振声信号时频分析研究与应用[D].济南:山东大学,2016.
[11]Iatsenko D,Mcclintock P V E,Stefanovska A.Linear and synchrosqueezed time-frequency representations revisited:Overview,standards of use,resolution,reconstruction,concentration,and algorithms[J].Digital Signal Processing,2015,42:1-26.