基于小波和EMD的时频矩阵DEM降噪方法
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摘要
针对强噪声背景下振动信号容易被非平稳强噪声干扰问题,提出了一种小波和EMD结合的时频矩阵DEM降噪方法,该方法拟解决小波变换对信号局部自适应性差的问题,同时增强EMD本征模态函数的选取和重构。通过该方法对仿真信号和实测振动信号降噪,结果表明:该方法能够有效提取强噪声背景下有效的振动信号,提高信噪比。这一方法为旋转机械在强噪声干扰运行环境下提取有效故障信息提供支持。
For the non-stationary interference problem of vibration signal in the background of strong noise, the time frequency matrix DEM de-noising method combining wavelet and EMD was proposed to solve the poor local adaptability of wavelet transformation to signal and reinforce the selection and reconstruction of the EMD IMFs. The method was employed to de-noise simulated and actual vibration signals, and the findings indicated that the method could be effective in extracting valid vibration signal in the background of strong noise and increasing the signal-to-noise ratio. The method offers support for the extraction of valid fault information from rotating machines in the operating environment of strong noise disturbance.
For the non-stationary interference problem of vibration signal in the background of strong noise, the time frequency matrix DEM de-noising method combining wavelet and EMD was proposed to solve the poor local adaptability of wavelet transformation to signal and reinforce the selection and reconstruction of the EMD IMFs. The method was employed to de-noise simulated and actual vibration signals, and the findings indicated that the method could be effective in extracting valid vibration signal in the background of strong noise and increasing the signal-to-noise ratio. The method offers support for the extraction of valid fault information from rotating machines in the operating environment of strong noise disturbance.
引文
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[2] 李红,孙冬梅,沈玉成.EEMD降噪与倒频谱分析在风电轴承故障诊断中的应用[J].机床与液压,2018,46(13):156-159.
[3] 侯蒙蒙,许同乐.基于DT-LMD机床轴承故障信号提取研究[J].机床与液压,2015,43(7):185-188.
[4] 李兴慧,胡小青.基于Gabor变换的特征提取方法[J].组合机床与自动化加工技术,2014(1):29-30.
[5] 周晓峰,杨世锡,甘春标.一种旋转机械振动信号的盲源分离消噪方法[J].振动、测试与诊断,2012,32(5):714-717,858-859.
[6] 王振华,潘宏侠,刘静.基于粒子滤波信号处理的柴油机故障诊断[J].煤矿机械,2013,34(5):301-303.
[7] QIAO Z J,PAN Z R.SVD principle analysis and fault diagnosis for bearings based on the correlation coeffi-cient[J].Measurement Science and Technology,2015,26(8):085014.
[8] 陈骏杰,师蔚,胡定玉.基于IMF聚合与SVD的城轨车辆牵引电机轴承故障诊断[J].测控技术,2017,36(1):14-17,22.
[9] 李艳艳.双严格对角占优矩阵最小特征值得下界[J].云南民族大学学报(自然科学版),2017,26(3):209-211.