Computer Science ›› 2019, Vol. 46 ›› Issue (6A): 537-539.

• Interdiscipline & Application • Previous Articles     Next Articles

Vibration Sensor Data Analysis Based on Wavelet Denoising

ZHANG Yang-feng1, WEI Shi-hong1, DENG Na-na2, WANG Wen-rui3   

  1. Sinohydro Bureau 7 Co.,Ltd.,Chengdu 610000,China1;
    Shanghai Unitoon Information Technology Co.,Ltd.,Shanghai 201210,China2;
    Shanghai Advanced Research Institute,Chinese Academy of Sciences,Shanghai 201210,China3
  • Online:2019-06-14 Published:2019-07-02

Abstract: Aiming at the problems of signal filtering and fault signal data preservation and extraction in vibration data of mining machinery and equipment,this paper proposed a wavelet transform method based on neural network optimization threshold.The MEMS triaxial accelerometer is used to sample the digital data,then converted into displacement by the processing,and then the wavelet decomposition is performed.The high-frequency coefficients are optimized and adjusted by neural network threshold,and the data are reconstructed to achieve the effect of noise reduction.Finally,Fourier transform of filtered signals is carried out,and the ratio of high-frequency coefficients is calculated according to amplitude frequency energy.Experimental results show that the wavelet transform method based on neural network adjusting threshold can automatically adjust the threshold after adaptive learning,and has ideal filtering effect on vibration sensor signal.The high frequency noise energy can be filtered more than 15% than the traditional threshold,and can retain abrupt fault information,which provides an important basis for later fault diagnosis.

Key words: Data processing, Neural network, Threshold optimization, Vibration sensor, Wavelet transform

CLC Number: 

  • TM937
[1]古德生.地下金属矿采矿科学技术的发展趋势[J].黄金,2004,25(1):18-22.
[2]李舜酩,郭海冬,李殿荣.振动信号处理方法综述[J].仪器仪表学报,2013,34(8):1907-1915.
[3]CHANG S G,YU B,VETTERLI M.Adaptive Wavelet thre-sholding for image denoising and compression[J].IEEE Trnasactions on Image Processing,2000,9(9):1532-1246.
[4]丁锋,秦峰伟.小波降噪及Hilbert变换在电机轴承故障诊断中的应用[J].电机与控制学报,2017,21(6):89-95.
[5]WANG J F,LI F G.Signal processing method in the mechanical fault diagnosis technology:The time domain analysis[J].Noise and Vibration Control,2013,1355(1):128-132 [6]王江萍,孙文莉.基于小波包能量谱齿轮振动信号的分析与故障诊断[J].机械传动,2011,35(1):55-58.
[8]HASHMI M,LEHTONEN M,NORDMAN M,et al.Wavelet based de-noising of on-line PD signals captured by Pearson coil in covered-conductor overhead distribution networks[J].International Journal of Electrical Power & Energy Systems,2012,43(1):1185-1192.
[9]颜世玉,刘冲,赵海滨,等.基于小波包分解的意识脑电特征提取[J].仪器仪表学报,2012,33(8):1914.
[10]KONAR P,CHATTOPADHYAY P.Bearing Fault Detection of Induction Motor Using Wavelet and Support Vector Machines(SVMs) [J] .Applied Soft Computing,2011,11(6):4203-4211.
[11]NEL I Y,BENBOUZID M E H.Induction Motors Bearing Failures Detection and Diagnosis:Park and Concordia Transform Approaches Comparative Study[J] .IEEE /ASME Transactions on Mechatronics,2008,13(2):257-262.
[12]CONG F,CHEN J,DONG G,et al.Vibration Model of Rolling Element Bearings in a Rotor-Bearing System for Fault Diagnosis[J].Journal of Sound and Vibration,2013,332(8):2081-2097.
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