基于自动搜峰和shannon熵的车辆轴承多普勒畸变故障声信号校正研究
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摘要
针对传声器采集的运动声源信号存在多普勒畸变问题,提出一种基于自动搜峰和shannon熵的滚动轴承多普勒畸变故障声信号校正方法;首先对所采集的声音信号进行短时傅里叶(STFT)时频分析;然后利用自动搜峰方法进行瞬时频率估计,设置shannon熵来提高瞬时频率估计精度,并得到拟合的瞬时频率曲线,进而得到信号重采样时间点;最后对原信号进行时域重采样,从而使畸变信号得以矫正;通过仿真和动态滚动轴承内外圈故障声信号的实验验证了此种方法的可行性。
For the doppler distortion problem for motion source signals collected by microphones,the method for correcting the acoustic signal of a rolling bearing Doppler distortion based on automatic peak search and shannon entropy is proposed.Firstly,the collected sound signal is analyzed by short time Fourier(STFT)time-frequency analysis;then,the instantaneous frequency estimation is performed using the automatic peak search method,the Shannon entropy is set to improve the accuracy of the instantaneous frequency estimation,and the fitted instantaneous frequency curve is obtained.Then the signal re-sampling time point is obtained;in the end,the original signal is re-sampled in the time domain so that the distortion signal can be corrected.The feasibility of this method is verified by experiments using acoustic signals of the inner and outer rings of the simulation and dynamic rolling bearing.
For the doppler distortion problem for motion source signals collected by microphones,the method for correcting the acoustic signal of a rolling bearing Doppler distortion based on automatic peak search and shannon entropy is proposed.Firstly,the collected sound signal is analyzed by short time Fourier(STFT)time-frequency analysis;then,the instantaneous frequency estimation is performed using the automatic peak search method,the Shannon entropy is set to improve the accuracy of the instantaneous frequency estimation,and the fitted instantaneous frequency curve is obtained.Then the signal re-sampling time point is obtained;in the end,the original signal is re-sampled in the time domain so that the distortion signal can be corrected.The feasibility of this method is verified by experiments using acoustic signals of the inner and outer rings of the simulation and dynamic rolling bearing.
引文
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[14]朱瑜,王殿,王海洋,等.基于EMD和信息熵的滚动轴承故障诊断[J].轴承,2012(6):50-53.
[2]Dybala J,Radkowski S.Reduction of Doppler effect for the needs of wayside condition monitoring system of railway vehicles[J].Mechanical Systems&Signal Processing,2013,38(1):125-136.
[3]Stojanovic M,Catipovic J A,Proakis J G.Phase-coherent digital communications for underwater acoustic channels[J].Oceanic Engineering,1994,19(1):100-111.
[4]Johnson M,Freitag L,Stojanovic M.Improved Doppler Tracking and Correction for Underwater Acoustic Communications[A].IEEE International Conference on Acoustics,Speech,and Signal Processing[C].IEEE Computer Society,1997:575.
[5]杨殿阁,罗禹贡,李兵,等.基于时域多普勒修正的运动声全息识别方法[J].物理学报,2010,59(7):4738-4747.
[6]张翱,胡飞,沈长青,等.基于能量重心法的列车轴承多普勒畸变故障声信号校正诊断研究[J].振动与冲击,2014,33(5):1-7.
[7]张海滨,陆思良,何清波,等.多普勒畸变声学信号的伪时频分析及其校正[J].振动与冲击,2016,35(5):14-20.
[8]赵晓平,吴家新,周子贤.改进的Seam Carving瞬时频率估计算法研究[J].南京信息工程大学学报(自然科学版),2017,9(2):214-219.
[9]孔庆鹏,宋开臣,陈鹰.基于分段最小二乘拟合的瞬时频率估计方法[J].农业机械学报,2006,37(11):204-206.
[10]Morse P,Ingard K.Theoretical Acoustics[M].Science Press,Bearing,1986.
[11]Cline J E,Bilodeau J R.Acoustic Wayside Identification of Freight Car Roller Bearing Detects[A].Proceedings of the1998ASME/IEEE Joint Railroad Conference[C].1998.79-83.
[12]熊伟,张海滨,何清波,等.基于ROSST的列车轴承轨边声学信号校正与诊断研究[J].振动与冲击,2017,36(6):11-17.
[13]李泓锦,杨晓冬,李万军,等.一种非定比多轴系自适应阶比分析方法的研究[J].北华航天工业学院学报,2017,27(3):9-12.
[14]朱瑜,王殿,王海洋,等.基于EMD和信息熵的滚动轴承故障诊断[J].轴承,2012(6):50-53.