时空域相关自适应小波包声发射信号降噪方法
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摘要
现有的小波变换方法在对受强噪声干扰的声发射信号降噪时,由于忽略了小波系数之间的相关性,容易造成信号失真。针对该问题,提出了一种基于时空域相关性的自适应小波包声发射信号降噪方法。算法采用自适应冗余小波变换,最大限度保留了信号的时域空域特征,并利用小波系数在时域空域上存在的相关性,提高对声发射信号及噪声的甄别能力,提升降噪效果。同时利用小波系数在空域上的相关性寻找最佳分解层数,进一步提升了算法的适应性。为了验证本文提出算法的有效性,分别在模拟声发射信号和断铅信号上添加不同程度的噪声,并进行降噪实验。实验结果表明,与其他算法相比,经过本文算法处理的信号信噪比大大提高,均方根误差也有一定程度的降低。
Existing wavelet transform method in the strong noise interference AE signal de-noising,due to ignore the correlation between the wavelet coefficients,easily lead to signal distortion. To solve the problem,a time-spatial correlation based adaptive wavelet packet method was proposed to Acoustic Emission( AE) signal de-noising. Adaptive redundant wavelet transform was used to maximize the time-spatial features in the algorithm. The correlation of the wavelet coefficients in time-spatial domain were used to improve the discrimination ability of AE signal and noise,It were improves the effect of noise reduction. Meanwhile,the correlation of wavelet coefficients in spatial domain was used to find the best decomposition level to further improve the adaptability of the algorithm.In order to validate the effectiveness of the proposed algorithm,different levels of noise are added to the simulated AE signal and the disconnected lead signal respectively,and the noise reduction experiment is carried out. Experimental results show that the SNR of the signal processed by this algorithm is greatly improved compared with that of the other algorithms,and the RMSE is also reduced to a certain extent.
Existing wavelet transform method in the strong noise interference AE signal de-noising,due to ignore the correlation between the wavelet coefficients,easily lead to signal distortion. To solve the problem,a time-spatial correlation based adaptive wavelet packet method was proposed to Acoustic Emission( AE) signal de-noising. Adaptive redundant wavelet transform was used to maximize the time-spatial features in the algorithm. The correlation of the wavelet coefficients in time-spatial domain were used to improve the discrimination ability of AE signal and noise,It were improves the effect of noise reduction. Meanwhile,the correlation of wavelet coefficients in spatial domain was used to find the best decomposition level to further improve the adaptability of the algorithm.In order to validate the effectiveness of the proposed algorithm,different levels of noise are added to the simulated AE signal and the disconnected lead signal respectively,and the noise reduction experiment is carried out. Experimental results show that the SNR of the signal processed by this algorithm is greatly improved compared with that of the other algorithms,and the RMSE is also reduced to a certain extent.
引文
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[2]Bashkov O V,Romashko R V,Zaikov V I,et al.Detecting acoustic-emission signals with fiber-optic interference transducers[J].Russian Journal of Nondestructive Testing,2017,53(6):415-421.
[3]Zvokelj M,Zupan S,Prebil I.EEMD-based multiscale ICA method for slewing bearing fault detection and diagnosis[J].Journal of Sound and Vibration,2016,370:394-423.
[4]Han L,Li C W,Guo S L,et al.Feature extraction method of bearing AE signal based on improved FAST-ICA and wavelet packet energy[J].Mechanical Systems and Signal Processing,2015,62-63:91-99.
[5]赵鸿图,刘云.基于三次样条插值的小波模极大值去噪算法[J].计算机工程与设计,2014(8):2965-2968.
[6]艾延廷,王帅,周海仑,等.空域相关和LMD结合的滚动轴承声发射信号特征提取[J].机械设计与制造,2016(8):171-174.
[7]Shi Y,Dong L H,Wang H D,et al.Fatigue features study on the crankshaft material of 42Cr Mo steel using acoustic emission[J].Frontiers of Mechanical Engineering,2016,11(3):233-241.
[8]Liu Z,Zou S,Li Z,et al.Wavelet energy features of acoustic emission signals under centrifugal pump cavitation conditions[J].Transactions of the Chinese Society of Agricultural Engineering,2015,31(8):99-103.
[9]孙伟,熊邦书,黄建平,等.小波包降噪与LMD相结合的滚动轴承故障诊断方法[J].振动与冲击,2012,31(18):153-156.
[10]郭玉荣,崔玲丽,高立新,等.冗余提升小波的最优分解层数的确定[J].机械设计与制造,2014(11):147-149.
[11]黎锁平,周勇,周永强.自适应小波阈值去噪算法在低空飞行声目标的应用[J].振动与冲击,2017,36(9):153-158.
[12]隋文涛,张丹.基于峭度的阈值降噪方法及在振动信号分析中应用[J].振动与冲击,2013,32(7):155-158.
[13]Li L.On the block thresholding wavelet estimators with censored data[J].Journal of Multivariate Analysis,2008,99(8):1518-1543.
[14]吴定海,王怀光,张培林,等.基于双树复小波包变换的空域和邻域联合降噪[J].华中科技大学学报:自然科学版,2015,43(4):17-21.
[15]廖强,李迅波,陈勇强.自适应冗余提升多小波包在滚动轴承复合故障诊断中的应用[J].机械设计与制造,2015(6):21-24.