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基于独立元分析的数据重构方法及应用
引用本文:钟蕾,刘飞. 基于独立元分析的数据重构方法及应用[J]. 系统仿真学报, 2007, 19(17): 4090-4092,4096
作者姓名:钟蕾  刘飞
作者单位:江南大学自动化研究所,无锡,江苏,214122
基金项目:教育部跨世纪优秀人才培养计划
摘    要:在工业系统采集数据的过程中,因为种种原因会发生数据遗失的现象。为了更好的对工业过程进行分析评估、优化及监控,往往需要重构遗失的数据。主元分析法(PCA)常用于重构遗失数据,但是由于PCA要求观测数据服从正态分布,而实际工业系统获得的数据往往很难满足条件。因此提出一种基于独立元分析(ICA)的数据重构方法。首先使用在正常运行情况下获得的原始数据建立ICA模型,然后利用相关的监控统计量规则来重构遗失的数据,最后通过在TE过程上的仿真应用,验证了该方法的可行性及与PCA相比较的优越性。

关 键 词:数据遗失  数据重构  独立元分析法  TE过程
文章编号:1004-731X(2007)17-4090-03
收稿时间:2006-07-03
修稿时间:2006-07-032006-09-13

Data Reconstruction and Application Based on Independent Component Analysis
ZHONG Lei,LIU Fei. Data Reconstruction and Application Based on Independent Component Analysis[J]. Journal of System Simulation, 2007, 19(17): 4090-4092,4096
Authors:ZHONG Lei  LIU Fei
Affiliation:Institute of Automation, Southern Yangtze University, Wuxi 214122, China.
Abstract:During the sampling in industrial system, there are various reasons resulting in the cases of data missing. In order to evaluate, optimize and monitor the industrial process more effectively, missing data usually needs to be reconstructed. Principal component analysis (PCA) is usually used for reconstructing missing data, but it requires that measured data must be subject to normal probability distribution, which sometimes can not be satisfied in actual industrial system. A method of data reconstruction was introduced based on independent component analysis (ICA). ICA model was built using the measured data under normal operation condition (NOC), and then missing data was reconstructed by the correlative monitoring indices, and the simulation on TE process was carried out to prove the feasibility of the method and the advantage comparing with PCA.
Keywords:Missing data   Data reconstruction   ICA   TE process
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