期刊文献+

基于隐Markov模型的机械振动源数估计方法

Mechanical Vibration Source Number Estimation Based on Hidden Markov Models
下载PDF
导出
摘要 针对非平稳时变信号,提出一种基于隐Markov模型(HMM)的机械振动源数估计方法。该方法结合隐Markov模型理论与自相关测定,通过比较不同模型的信度来确定信源的个数。实验结果表明该方法能够有效地估计出非平稳时变信号的信源个数,为机械振动故障诊断中的振动源分析提供了方法保障。 To estimate the number of mechanical vibration sources from nonstationary and timevarying signals,a new method based on hidden markov models (HMM) is proposed. The method combined hidden Markov models with automatic relevance determination to determine the number of sources through comparing the reliability of different models. The experimental results show that the method can effectively estimate the number of nonstationary and time varying signal sources,and this work is helpful to analyze the mechanical vibrations in fault monitoring system.
出处 《机械工程师》 2013年第5期13-15,共3页 Mechanical Engineer
关键词 隐MARKOV模型 自相关测定 故障诊断 源数估计 hidden markov models automatic relevance determination fault diagnosis source number estimation
  • 相关文献

参考文献4

  • 1MACKAY D J C. Ensemble learning for hidden Makov models[D].Cambridge,UK:University of Cambridge,1997.
  • 2CHOUDREY R A. Variational method for bayesian independent component analysis[D].UK:University of Oxford,2002.
  • 3REZEK I. Bayesian inference for Hidden Markov Models[J].Electronics Letters,2001.91-94.
  • 4范涛,李志农,肖尧先.基于源数估计的机械源信号盲分离方法研究[J].机械强度,2011,33(1):15-19. 被引量:7

二级参考文献5

  • 1李熠,何永勇,李志农,褚福磊.盲源分离和小波消噪在碰摩声频信号分析中的应用研究[J].机械强度,2005,27(6):719-724. 被引量:9
  • 2Jordan M I. l_earning in Graphical Models [ M ]. Cambridge, Massachusetts, USA: The M1T Press, 1999: 12-35.
  • 3MacKay D J C. Probable networks and plausible prediction a review of practical Bayesian methods of supervised neural networks[J]. Computation in Neural Systems, 1995(6) : 469-505.
  • 4Miskin J W. Ensemble learning for independent component analysis[ D]. The United Kingdom: University of Cambridge, 2000: 35-48.
  • 5Jordan M I, Jaakkola T S, Bayesian parameter estimation via variational methods[J]. Statistics and Computing, 2000(10): 25-37.

共引文献6

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