期刊文献+
任意字段
题名或关键词
题名
关键词
文摘
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息

基于Hilbert谱图特征的转子故障智能诊断 被引量：6

Intelligent Diagnosis of Aero-engine Rotor Fault by Hilbert Spectrum
下载PDF
导出
摘要 提出一种基于Hilbert谱图特征的转子故障智能诊断方法。首先,通过希尔伯特-黄变换(HHT)得到反映故障信号特征的Hilbert谱;然后,利用主成分分析(PCA)对故障信号的Hilbert谱进行特征提取;最后,对得到的特征数据使用野点检测进行分类,并用粒子群优化算法,自适应获得野点检测最优参数,实现转子故障的智能诊断。使用ZT-3型转子故障试验台实验数据对此方法进行了验证,并与传统频谱特征分类结果进行比较,结果表明了此方法的正确性。 A method for intelligent diagnosis of rotor fault based on the characteristics of Hilbert spectrum is proposed.First,Hilbert spectrums with fault characteristics are obtained through Hilbert-Huang transformation.Then,characteristics of Hilbert spectrum are extracted by principal component analysis（PCA）.Finally,characteristics are classified using novelty detection,and parameters are optimized by particle swarm optimization,thus intelligent diagnosis is realized.In addition,the data from ZT-3 multiple-function experimental instrument are compared with those by the method of traditional frequency spectrum.The comparison confirmed the validity of our method.
作者 谭真臻 陈果 孙丽萍
机构地区 南京航空航天大学民航学院
出处 《机械科学与技术》 CSCD 北大核心 2010年第9期1177-1181,共5页 Mechanical Science and Technology for Aerospace Engineering
基金 国家自然科学基金项目(50705042) 航空科学基金项目(2007ZB52022)资助
关键词 希尔伯特-黄变换 Hilbert谱 主成分分析 野点检测 粒子群算法 Hilbert-Huang transformation Hilbert spectrum PCA novelty detection particle swarm optimization
分类号 V263.6 [航空宇航科学与技术—航空宇航制造工程] TP277 [自动化与计算机技术—检测技术与自动化装置]
  • 相关文献

参考文献11

  • 1Huang N E, Shen Z. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis [ A ]. Proceedings of the Royal Society of London [C], 1998.
  • 2Huang N E. Computer Implicated Empirical Mode Decamposition Method, Apparatus, and Article of Manufacture [P]. USA Provisional Application, 1999.
  • 3Loh C H, Wu T C, Huang N E. Application of the empirical mode decomposition-Hflbert spectrum method to identify nearfault ground-motion characteristics and structural responses[J]. Bulletin of the Seismological Society of America, 2001, (91).
  • 4Vasudevan K, Cook F A. Empirical mode skeletonization of deep crustal seismic data: Theory and applications [J]. Journal of Geophysical Research, 2000, (105) :7845 -7856.
  • 5Echeverria J C, et al. Application of empirical mode decomposition to heart rate variability analysis[J]. Medical & Biological Engineering & Computing, 2001, (39):471 -479.
  • 6Richard O. Duda, Peter E. Hart, David G. Stork.模式分类[M].机械工程出版社,2003.
  • 7Larry M M, Malik M. One-class SVMs for document classification [J]. Journal of Machine Learning Research, 2001 ,(2).
  • 8Camci F, Chinnam R B. General support vector representation machine for one-class classification of non-stationary classes [ J]. Pattern Recognition, 2008,41:3021-3034.
  • 9陈果.基于遗传算法的支持向量机分类器模型参数优化[J].机械科学与技术,2007,26(3):347-350. 被引量：40
  • 10Sift Y, Eberhart R C. Poarticle swarm optimization: developments applications and resources [ A]. Proceedings of the IEEE Congress on Evolutionary Computation [ C ]. Piscataway, USA: IEEE Service Center,2001:81 -86.

二级参考文献13

  • 1吴佑寿,赵明生,丁晓青.一种激励函数可调的新人工神经网络及应用[J].中国科学（E辑）,1997,27(1):55-60. 被引量：26
  • 2Eberhart R C,Kennedy J. A new optimizer using particle swarm theory [A]. Proceedings of the Sixth International Symposium on Micro Machine and Human Science [C]. Piscataway, USA: IEEE Service Center, 1995. 39-43.
  • 3Eberhart R C,Shi Y H. Particle swarm optimization: developments, applications and resources [A]. Proceedings of the IEEE Congress on Evolutionary Computation [C]. Piscataway, USA: IEEE Service Center, 2001. 81-86.
  • 4Shi Y H,Eberhart R C. Fuzzy adaptive particle swarm optimization [A]. Proceedings of the IEEE Congress on Evolutionary Computation [C]. Piscataway, USA: IEEE Service Center, 2001. 101-106.
  • 5Shi Y H, Eberhart R C. A modified particle swarm optimizer [A]. Proceedings of the IEEE Congress on Evolutionary Computation [C]. Piscataway,USA: IEEE Service Center, 1998. 69-73.
  • 6Vapnik V.The Nature of Statistical Learning[M].New York:Springer,1995.
  • 7Ge M,Du R,Zhang C C,Xu Y S.Fault diagnosis using support vector machine with an application in metal stamping operations[J].Mechanical Systems and Signal Processing,2004,18:143-159.
  • 8Goldberg D.Genetic Algorithms in Search,Optimization and Machine Learning[M].Addison-Wesley,Reading,MA,1989.
  • 9Baum E B,Lang K L.Constructing hidden units using examples and queries[A].In:Advances in Neural Information Processing System3[C],San Maeto.CA:Morgan Kaufmann,1991:904-910.
  • 10Chen Q C.Generating-shrinking algorithm for learning arbitary classfication[J].Neural Networks,1994,7:1477-1489.

共引文献176

同被引文献290

引证文献6

二级引证文献45

机械科学与技术
2010年 第9期

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