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基于数据融合的多变量相空间重构方法

丛 蕊 刘树林 马 锐

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基于数据融合的多变量相空间重构方法

丛 蕊, 刘树林, 马 锐

An approach to phase space reconstruction from multivariate data based on data fusion

Cong Rui, Liu Shu-Lin, Ma Rui
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  • 针对单变量时间序列和多变量时间序列相空间重构所存在的问题,提出一种新的多变量融合的相空间重构方法. 通过Bayes估计理论,将多变量在同一相空间中进行相点的最优融合,得到了更为理想的融合相空间. 应用所提出的方法对Lorenz系统及耦合Rssler系统进行了多变量融合的相空间重构. 通过多变量重构图与单变量重构图的比较,发现基于数据融合的多变量相空间重构图包含了所有单变量相空间重构图的重要信息,使重构的相空间更加完备,较全面地反映出吸引子的全貌信息. 最后应用该方法对转子油膜涡动故障得到的多变量时间序列
    To cope with the problem of phase space reconstruction from univariate time series and multivariate time series, the novel approach to phase space reconstruction from multivariate data based on data fusion is presented in this paper. According to Bayes estimation theory, the phase points in the same phase space reconstructed from multivariate data are fused, then an optimal fusion phase space could be determined. The approach is applied to multivariate phase space reconstructions of Lorenz system and Rssler system, respectively. Compared with the figures reconstructed from univariate datas, the information reconstructed from multivariate data includes the main characters of all univariate data and represents the comprehensive information of system attractor, which makes the phase space reconstructed more abundant. At last, the approach is applied to multivariate phase space reconstruction of oil film whirling in the rotor system. The information reconstructed includes all the characters of the system, which improves the veracity for fault diagnosis. So all the analysis further shows that the approach presented here is effective.
    • 基金项目: 黑龙江省研究生创新基金(批准号:YJSCX2007-0145HLJ)资助的课题.
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  • 文章访问数:  8402
  • PDF下载量:  2020
  • 被引次数: 0
出版历程
  • 收稿日期:  2007-10-13
  • 修回日期:  2008-06-04
  • 刊出日期:  2008-06-05

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