A fusion method of multisource data using phase space reconstruction

被引：3
|
作者：
Zhao H. [1 ]
Gao Z. [1 ]
Gao J. [1 ]
Wang R. [1 ]
机构：
[1] State Key Laboratory for Manufacturing System Engineering, Xi'an
来源：
Hsi An Chiao Tung Ta Hsueh | / 8卷 / 84-89期
关键词：
Adaptive weighted fusion estimation; Data fusion; Information entropy; Phase space reconstruction;
D O I：
10.7652/xjtuxb201608014
中图分类号：
学科分类号：
摘要：
A new fusion technology for multi-source data based on the phase space reconstruction is proposed to focus on the problem of multivariable and high redundancy of the condition monitoring variables in the chemical production system. Both the mutual information method and the Cao method are used to select the reconstruction parameters, the time delay and the embedding dimension. Then, the information entropy is employed to obtain an improved objective function in adaptive weighted fusion estimating method for multisource data fusion, and the weighting coefficients of various information sources are calculated by means of a social cognitive optimization algorithm. The effectiveness of the proposed method is verified by an analysis of one case study of real chemical plant data sets. The results and a comparison with the traditional method show that the proposed method gets improvements in the amount of information and average PSNR, respectively. It is concluded that the proposed method improves the completeness of the information of the reconstructed phase space and provides a new approach for the multi-source data fusion of heterogeneous sensors. © 2016, Editorial Office of Journal of Xi'an Jiaotong University. All right reserved.
引用
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页码：84 / 89
页数：5
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