期刊文献+

双谱分析及其在滚动轴承故障诊断中的应用 被引量:6

Bispectrum analysis and its application in thefault diagnosis of rolling rearing
在线阅读 下载PDF
导出
摘要 双谱是处理非线性、非高斯性信号的有力工具,它包含了高阶谱的所有特性.针对滚动轴承具有非线性和非高斯的特性,利用双谱分析方法研究了不同故障模式下滚动轴承的双谱特性以及同一故障类型在不同程度时的双谱特性.实验结果表明,利用双谱特性能很好地区分滚动轴承的不同故障模式以及故障的严重程度,双谱分析方法在滚动轴承故障诊断中具有良好的工程应用前景. Bispectrum, which has all characteristics of higher-order spectrum, is a powerful processing tool for non-linear and non-Gaussian signal. Because the signal from rolling bearing is non-linear and non-Gaussian, here, the bispectrum analysis method is applied to the different types of rolling bearing and the different serious extent for the same bearing fault. The experiments show that the bispectrum analysis method is very effective in the fault diagnosis of rolling bearing. This method has a good prospect in the fault diagnosis of rolling bearing.
出处 《中国工程机械学报》 2005年第3期365-368,共4页 Chinese Journal of Construction Machinery
基金 河南省杰出人才创新基金资助项目
关键词 双谱分析 轴承 故障诊断 bispectrum analysis rolling bearing fault diagnosis
  • 相关文献

参考文献5

二级参考文献25

  • 1王威,张宁.高阶谱理论及其在雷达信号处理中的应用[J].电子器件,1997,20(1):243-248. 被引量:1
  • 2David logan, Joseph Mathew. Using Correlation Dimension for Vibration Fault Diagnosis of Rolling Element Bearing[J]. Basic Concept. Mechanical Systems and Signal Processing, 1996,10(3) :241 - 250.
  • 3Paya B A, Esat I I .Artificaial Neural Network Based Fault Diagnostics of Rotating Machinery Using Wavelet Transforms sa a Preprocessor .Mechanical Systems and Signal Processing 1997 .11(5):751-765.
  • 4Huang N E, et al .The Empirical Mode Decomposition and the Hilbert Spectrum for Nonlimear and Non-Stationary Time Series Analysis .Proc .R .Soc.Lond. A,1998,454:903-995.
  • 5张省 李立天 李卫东.钢包回转台低速重载轴承的监测诊断与预报[J].东北大学学报:自然科学版,1997,18:48-51.
  • 6Tolba A S. Wavelet packet compression of medical images[J]. Digital Signal Processing, 2002,12(4):441-470.
  • 7Mandischer M. A comparison of evolution strategies and back-propagation for neural network training[J]. Neurocomputing, 2002,42(3):87-117.
  • 8Balazinski M, Czogala E,Jemielniak K, et al. Tool condition monitoring using artificial intelligence methods[J]. Artificial Intelligence, 2002,15(6):73-80.
  • 9Gary G Y, Lin K C. Wavelet packet feature extraction for vibration monitoring[J]. IEEE Transactions on Industrial Electronics, 2000,47(3):650-667.
  • 10Daubechies I, Mallat S, Willsky A S. Introduction to the special issue on wavelet transforms and multiresolution signal analysis[J]. IEEE Trans Inform Theory, 1992,38(2):529-532.

共引文献215

同被引文献37

引证文献6

二级引证文献13

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