期刊文献+

自适应冗余第二代小波在信号去噪中的应用 被引量:4

Application of adaptive redundancy second generation wavelet in signal denoising
原文传递
导出
摘要 介绍了基于提升方法的第二代小波原理以及自适应冗余第二代小波的构造方法。将自适应冗余第二代小波和冗余第二代小波变换应用于提升机振动信号的去噪分析并进行比较。试验表明,相对于冗余第二代小波,自适应冗余第二代小波在信号去噪方面具有更好的特性。 The concept of second generation wavelet based on lifting method and the construction method of adaptive redundancy second generation wavelet were described.The adaptive redundancy second generation wavelet and the redundancy second generation wavelet were applied to the analysis of vibration signal denoising of the mine hoist.Test showed that the adaptive redundancy second generation wavelet was more efficient on signal denoising than the redundancy second generation wavelet.
出处 《矿山机械》 北大核心 2012年第2期39-43,共5页 Mining & Processing Equipment
关键词 自适应冗余计算 第二代小波 去噪 提升机振动信号 adaptive redundancy calculation second generation wavelet denoising vibration signal of hoist
  • 相关文献

参考文献7

  • 1刘树春,潘紫微,宋淼.第二代小波在振动信号去噪中新方法的研究[J].机械传动,2008,32(3):64-66. 被引量:11
  • 2W. Sweldens. The lifting scheme: A custom-design construction of biorthogonal wavelets. Applied and Computational Harmonic Analysis [J]. 1996, 3(2): 186-200.
  • 3W. Sweldens. The lifting scheme: A Construction of Second Generation Wavelets [J]. SIAM Journal of Mathematical Analysis, 1998, 29(2): 511-546.
  • 4Jeffrey Travis, Jim Kring. LabV1EW for Everyone: Graphical Programming Made Easy and Fun, Third Edition [M]. Prentice Hall. 2006. 7.
  • 5Roger L. Claypoole Jr, Richard G. Baraniuk, Robert D. Nowak. Adaptive wavelet transform via lifting [C]. IEEE International Conference on Acoustics, Speech and Signal Processing, 1998(3): 1513-1516.
  • 6Q. Pan, L. Zhang, G. Dai, et al. Two denoising methods by wavelet transform. IEEE Transactions on Image Processing [J]. 1999, 47(12) : 3401-3406.
  • 7张吉先,钟秋海,戴亚平.小波门限消噪法应用中分解层数及阈值的确定[J].中国电机工程学报,2004,24(2):118-122. 被引量:112

二级参考文献9

共引文献119

同被引文献28

  • 1张志斌,郑海起,唐力伟.自适应二代小波变换在振动信号降噪中的应用[J].机械强度,2006,28(z1):48-51. 被引量:3
  • 2赵建新,刘海平.火炮反后坐装置故障机理研究[J].军械工程学院学报,2004,16(4):35-40. 被引量:7
  • 3Pan Hang xia, Liu Hui ling, Wang Ai yu. Fault diagnosis of automa- ton based on EMD and close degree[ J]. Sensors and Transducers, 2013,160(2) :612 -618.
  • 4Specht D F. Probabilistic neural networks[ J]. Neural Netwo-rks, 1990,3(1) :109 -118.
  • 5Elif Derya ubeyli. Implementing eigenvector methods/prob - abilis- tic neural networks for anlysis of EEG signals [ J ]. Neural Net- works. 2008,59(9) :1410 - 1417.
  • 6Yaguo Lei, Zhengjia He, Yanyang Zi. A new approach to intelli- gent fault diagnosis of rotating machinery [ J]. Expert Systems with applications, 2008, 35 : 1593 - 1600.
  • 7Li Qiang, Wang Taiyong, Leng Yonggang. Engineering signal pro- cessing hased on adaptive step-changed stochastic resonance [ J ]. Mechanical Systems and Signal Processing, 2007, 21 : 2267 - 2279.
  • 8Shijin Wang, Jianbo Yu, Edzel Lapira, et al. A modified support vector data description based novelty detection approach for ma- chinery components [J]. Applied Soft Computing, 2013, 13(2) : 1193 - 1205.
  • 9Jie Wang, Hongying Du, Xiaojun Yao, et al. Using classification structure pharmacokinetic relationship (SCPR) method to predict drug bioavailability based on grid-search support vector machine [J]. Analytica Chimica Acta, 2007, 601 (2) :156 - 163.
  • 10姬东朝,宋笔锋,易华辉.基于概率神经网络的设备故障诊断及仿真分析[J].火力与指挥控制,2009,34(1):82-85. 被引量:27

引证文献4

二级引证文献11

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部