留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

基于FSS-kernel BSS方法的机械故障诊断

杨彦龙 程伟 常洪振

杨彦龙, 程伟, 常洪振等 . 基于FSS-kernel BSS方法的机械故障诊断[J]. 北京航空航天大学学报, 2012, (11): 1557-1561.
引用本文: 杨彦龙, 程伟, 常洪振等 . 基于FSS-kernel BSS方法的机械故障诊断[J]. 北京航空航天大学学报, 2012, (11): 1557-1561.
Yang Yanlong, Cheng Wei, Chang Hongzhenet al. Fault diagnosis based on blind source separation using kernel function with finite support samples[J]. Journal of Beijing University of Aeronautics and Astronautics, 2012, (11): 1557-1561. (in Chinese)
Citation: Yang Yanlong, Cheng Wei, Chang Hongzhenet al. Fault diagnosis based on blind source separation using kernel function with finite support samples[J]. Journal of Beijing University of Aeronautics and Astronautics, 2012, (11): 1557-1561. (in Chinese)

基于FSS-kernel BSS方法的机械故障诊断

详细信息
  • 中图分类号: TN911

Fault diagnosis based on blind source separation using kernel function with finite support samples

  • 摘要: 机械设备发生故障时,故障特征的提取是很重要的.为了从观测信号中分离出不同的故障特征源信号,并根据分离信号准确地进行故障诊断,从观测信号样本出发,提出了基于有限支持样本核函数的盲源分离(FSS-kernel BSS)方法.此方法利用有限的观测样本估计信号的概率分布,得到了评价函数,具有很好的自适应能力.仿真试验结果表明:此方法能成功地分离超、亚高斯混合信号,与其他盲源分离方法相比,此方法具有更好的分离性能.将该方法用于转子不平衡和支座松动的复合故障信号的盲分离,分离出了各复合故障的主要频谱.分离结果表明:此方法应用于机械设备复合故障诊断中是可行的.

     

  • [1] 李良敏,温广瑞,王生昌,等.机械故障诊断的遗传-独立分量分析算法[J].农业机械学报,2008,39(11):197-202 Li Liangmin,Wen Guangrui,Wang Shengchang,et al.Genetic algorithm based independent component analysis method in machine fault diagnosis[J].Transactions of the Chinese Society for Agricultural Machinery,2008,39(11):197-202(in Chinese)
    [2] 艾延廷,费成巍,张凤玲,等.ICA在航空发动机振动信号盲源分离中的应用[J].振动、测试与诊断,2010,30(6):671-674,711 Ai Yanting,Fei Chengwei,Zhang Fengling,et al.Blind source separation for aero-engines vibration signal by independent component analysis[J].Journal of Vibration,Measurement & Diagnosis,2010,30(6):671-674,711(in Chinese)
    [3] 胥永刚,李强,王正英,等.基于独立分量分析的机械故障信息提取[J].天津大学学报,2006,39(9):1066-1071 Xu Yonggang,Li Qiang,Wang Zhengying,et al.Fault information extraction of mechanical equipment based on independent component analysis[J].Journal of Tianjin University,2006,39(9):1066-1071(in Chinese)
    [4] 李良敏.基于遗传算法的盲源分离及其在轴承诊断中的应用[J].轴承,2005(9):31-34 Li Liangmin.Application of blind source separation method based genetic algorithm in bearing diagnosis[J].Bearing,2005(9):31-34 (in Chinese)
    [5] 吴作伦,杨世锡,冯海涛.基于最小互信息原理的机械振动源分离研究[J].机电工程,2003,20(5):44-46 Wu Zuolun,YangShixi,Feng Haitao.Study on separation of mechanical vibration based on the principle of the minimum mutual information[J].Mechanical & Electrical Engineering Magazine,2003,20(5):44-46(in Chinese)
    [6] 张洪渊,史习智.一种任意信号源盲分离的高效算法[J].电子学报,2001,29(10):1392-1396 Zhang Hongyuan,Shi Xizhi.An effective algorithm for blind separation of arbitrary source signals[J].Acta Electronica Sinica 2001,29(10):1392-1396(in Chinese)
    [7] Hyvarinen A,Oja E.A fast fixed point algorithm for independent component analysis[J].Neural Computation,1997,9(7):1483- 1492
    [8] Cardoso J F.High-order contrasts for independent component analysis[J].Neural Computation,1999,11(1):157-192
    [9] Duda R O,Hart P E, Strok D G.模式分类[M].李宏东,等译.2版.北京:机械工业出版社,2009:389-390 Duda R O,Hart P E,Strok D G.Pattern classification[M].Translated by Li Hongdong,et al.2nd ed.Beijing:China Machine Press Edition,2009:389-390(in Chinese)
    [10] Karvanen J,Eriksson J,Koivunen V.Pearson system based method for blind separation [C]//Proceedings of 2nd International Workshop on Independent Component Analysis and Blind Signal Separation.Helsinki:IEEE,2000:585-590
    [11] Barlow H.Unsupervised learning[J].Neural Computation,1989(1):295-311
    [12] Ozertem U,Uysal I,Erdogmus D.Continuously differentiable sample-spacing entropy estimation[J].IEEE Trans Neural Networks,2008,19(11):1978-1984
    [13] Bach F,Jordan M.Kernel independent component analysis[J].Journal of Machine Learning Research,2002(3):1-48
    [14] 成孝刚,姜华,刘国庆,等.基于参数Parzen窗估计的独立分量分析[J].信号处理,2009,25(3):485-488 Cheng Xiaogang,Jiang Hua,Liu Guoqing,et al.Independent component analysis based on parametric parzen window estimstion[J].Signal Processing,2009,25(3):485-488(in Chinese)
    [15] Ypma A,Leshem A.Blind separation of machine vibration with bilinear forms [C]//Proceedings of 2nd International Workshop on Independent Component Analysis and Blind Signal Separation.Helsinki:IEEE,2000:109-114
  • 加载中
计量
  • 文章访问数:  1783
  • HTML全文浏览量:  138
  • PDF下载量:  565
  • 被引次数: 0
出版历程
  • 收稿日期:  2011-07-28
  • 网络出版日期:  2012-11-30

目录

    /

    返回文章
    返回
    常见问答