基于小波变换和神经网络的车内噪声信号重构
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摘要
为获取较高精度车内噪声主动控制(Active Noise Control,ANC)参考信号,提出了一种基于小波变换和BP神经网络的车内噪声信号重构方法;以在某轿车采集到的噪声信号为基础,用声学传递路径分析(TPA)方法确定影响车内噪声的关键点信号;鉴于噪声源信号对车内信号非线性关系的复杂性,建立BP神经网络的噪声重构模型,并利用小波分解来降低噪声信号的非平稳性;为对比重构效果,建立BP神经网络噪声重构模型;结果表明,文章提出算法的重构值与实测值之间的平均绝对误差比BP神经网络小,并且基于小波变换和BP网络重构模型的平均绝对误差均小于0.01;该方法能够对车内噪声信号进行准确、有效的重构。
To obtain high-precision active noise control(ANC)reference signal,a reconstruction method of interior noise signals that based on wavelet transform and BP neural network was proposed.Based on the noise signal sources collected in a vehicle,the key point signals affecting the interior noise were determined using the acoustic transfer path analysis(TPA)method.In view of the complexity nonlinear relationship between the noise source signals and interior signals,a BP neural network reconstruction model was established.And then wavelet decomposition method was used to reduce the non-stationarity of signals.Comparing the reconstruction effect,a BP neural network was established at the same time.The results show that the average absolute error between the proposed method reconstruction values and the measured values is smaller than that of the BP neural network.And the average absolute error of BP network reconstruction model based on wavelet transform is less than 0.01.This method can be used to reconstruct the noise signals on passenger ear-sides accurately and effectively.
To obtain high-precision active noise control(ANC)reference signal,a reconstruction method of interior noise signals that based on wavelet transform and BP neural network was proposed.Based on the noise signal sources collected in a vehicle,the key point signals affecting the interior noise were determined using the acoustic transfer path analysis(TPA)method.In view of the complexity nonlinear relationship between the noise source signals and interior signals,a BP neural network reconstruction model was established.And then wavelet decomposition method was used to reduce the non-stationarity of signals.Comparing the reconstruction effect,a BP neural network was established at the same time.The results show that the average absolute error between the proposed method reconstruction values and the measured values is smaller than that of the BP neural network.And the average absolute error of BP network reconstruction model based on wavelet transform is less than 0.01.This method can be used to reconstruct the noise signals on passenger ear-sides accurately and effectively.
引文
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[16]佘琪,周鋐.传递路径分析用于车内噪声贡献量的研究[J].汽车技术,2010(3):16-19,30.
[17]林琳.基于压缩感知的信号重构与分类算法研究[D].西安:西安电子科技大学,2012.
[2]Huang Y,Jiang W K,Xin Y Z,et al.An experimental research of the characteristics of speeding-up interior noise of an intermediate coach[J].Journal of Vibration Engineering,2004:1116-1118.
[3]耿传飞,卢文良,俞醒.基于L-M算法的神经网络在环境振动分析中消除本底振动的应用[J].振动与冲击,2016,35(13):14-19.
[4]苏丽俐,王登峰,王倩.基于GRNN的车内噪声品质预测[J].吉林大学学报(工学版),2011,41(S2):82-86.
[5]Dietterieg T.Machine-learning research:four current directions[J].AI Mag.1998,18:97-136.
[6]程静,王维庆,何山.基于回归分析与BP神经网络的风机噪声预测[J].噪声与振动控制,2013,33(6):49-52.
[7]Tang J,Yu W,Chai T Y,et al.Selective ensemble modeling load parameters of ball mill based on multi-scale frequency spectral features and sphere criterion[J].Mech.Syst.Signal Process,2016:485-504.
[8]Feng Z,Liang M,Chu F.Recent advances in time-frequency analysis methods for machinery fault diagnosis:a review with application examples[J].Mechanical Systems and Signal Processing,2013,38(1):165-205.
[9]An X,Jiang D,Liu C,et al.Wind farm power prediction based on wavelet decomposition and chaotic time series[J].Expert Systems with Applications,2011,38(9):11280-11285.
[10]Gilles J.Empirical wavelet transform[J].IEEE Transactions on Signal Processing,2013,61(16):3999-4010.
[11]Feng Z,Zuo M J.Vibration signal models for fault diagnosis of planetary gearboxes.Journal of Sound and Vibration,2012,331(22):4919-4939.
[12]Cusido J,Romeral L,Ortega J A.Garcia Espinosa.Fault detection in induction machines using power spectral density in wavelet decomposition[J].IEEE Trans.Ind.Electron,2008:633-643.
[13]MacQueen J.Some methods for classification and analysis of multivariate observations[Z].Proc Fifth Berkeley Symp Math Stat Probab.1967,14(1):281-297.
[14]刘旺林.全频段车内噪声预测分析与控制研究[D].重庆:重庆大学,2016.
[15]Jia W,Zhang T,Dong G,et al.Correlation analysis of car exterior and interior noise tests under different yaw angles with beamforming[A].International Forum on Mechanical,Control and Automation[C].2017.
[16]佘琪,周鋐.传递路径分析用于车内噪声贡献量的研究[J].汽车技术,2010(3):16-19,30.
[17]林琳.基于压缩感知的信号重构与分类算法研究[D].西安:西安电子科技大学,2012.