期刊文献+

Application of particle swarm optimization blind source separation technology in fault diagnosis of gearbox 被引量:5

Application of particle swarm optimization blind source separation technology in fault diagnosis of gearbox
下载PDF
导出
摘要 Blind source separation (BBS) technology was applied to vibration signal processing of gearbox for separating different fault vibration sources and enhancing fault information. An improved BSS algorithm based on particle swarm optimization (PSO) was proposed. It can change the traditional fault-enhancing thought based on de-noising. And it can also solve the practical difficult problem of fault location and low fault diagnosis rate in early stage. It was applied to the vibration signal of gearbox under three working states. The result proves that the BSS greatly enhances fault information and supplies technological method for diagnosis of weak fault. Blind source separation (BBS) technology was applied to vibration signal processing of gearbox for separating different fault vibration sources and enhancing fault information. An improved BSS algorithm based on particle swarm optimization (PSO) was proposed. It can change the traditional fault-enhancing thought based on de-noising. And it can also solve the practical difficult problem of fault location and low fault diagnosis rate in early stage. It was applied to the vibration signal of gearbox under three working states. The result proves that the BSS greatly enhances fault information and supplies technological method for diagnosis of weak fault.
出处 《Journal of Central South University》 SCIE EI CAS 2008年第S2期409-415,共7页 中南大学学报(英文版)
基金 Project(50875247) supported by the National Natural Science Foundation of China Project(2007011070) supported by the Natural Science Foundation of Shanxi Province, China
关键词 PSO BLIND source SEPARATION FAULT diagnosis FAULT information enhancement GEARBOX PSO blind source separation fault diagnosis fault information enhancement gearbox
  • 相关文献

参考文献5

二级参考文献90

  • 1李晓欧,张笑微,冯焕清.一种新的变步长ICA自适应算法[J].电路与系统学报,2004,9(6):113-117. 被引量:3
  • 2张西宁,穆安乐,温广瑞.一种新的盲声源信号分离方法及其应用[J].西安交通大学学报,2005,39(1):6-8. 被引量:6
  • 3孙守宇,郑君里,吴里江,赵莹.峭度自适应学习率的盲信源分离[J].电子学报,2005,33(3):473-476. 被引量:11
  • 4郝志华,马孝江,王奉涛.非平稳信号的盲源分离在机械故障诊断中的应用[J].振动与冲击,2006,25(1):110-114. 被引量:15
  • 5[1]AMARI S,CICHOCKI A.Adaptive blind signal processing-neural network approaches[J].Proceedings IEEE,1998,86:186-187.
  • 6[2]AMARI S,DOUGLAS S C,CICHOCKI A,et al.Novel On-line Adaptive Learning Algorithms for Blind Deconvolution Using the Natural Gradient Approach[C]//In Proc 11th IFAC Symposium on System Identification,1997,3:1057-1062.
  • 7[3]CICHOCKI A,AMARI S.Adaptive Blind Signal and Image Processing[M].London:John Wiley & Sons Ltd,2002.
  • 8[4]SEUNGJIN CHOI,ANDRZEJ CICHOCKI,HYUNG-MIN PARK,et al.Blind source separation and independent component analysis:a review[J].Neural Information Processing-Letters and Reviews,2005,6(1):1-57.
  • 9[5]LIN QIU-HUA,YIN FU-LIANG.Blind source separation applied to image cryptosystems with dual encryption[J],Electronics Letters,2002,38(19):.1366-1369.
  • 10[6]CARDOSO J F,DELABROUILLE J,PATANCHON G.Independent Component Analysis of the Cosmic Microwave Background[C]//ICA2003,Nara:1111-1116.

共引文献34

同被引文献26

引证文献5

二级引证文献14

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