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基于对数变换和最大信息系数PCA的过程监测
Alternative TitleProcess Monitoring Based on Logarithmic Transformation and Maximal Information Coefficient-PCA
王中伟; 宋宏; 李帅; 周晓锋
Department数字工厂研究室
Source Publication科学技术与工程
ISSN1671-1815
2017
Volume17Issue:16Pages:259-265
Contribution Rank1
Funding Organization辽宁省科学技术计划项目(2015106015)资助
Keyword主元分析方法 最大信息系数 对数变换 过程监测
Abstract主元分析(principal component analysis,PCA)被广泛应用于工业生产过程监测。PCA假设数据服从高斯分布且协方差矩阵仅能评估变量间的线性关系,无法衡量变量间非线性依赖程度。基于此,提出了一种基于对数变换和最大信息系数(maximal information coefficient,MIC)PCA的过程监测方法。首先,应用对数变换对过程数据进行变换,在一定程度上改善数据分布。然后,采用可以度量变量间的非线性相关性的MIC矩阵替换协方差矩阵,从而改善对非线性非高斯过程的监测效果。最后通过在田纳西-伊斯曼过程(tennessee eastman process,TE)仿真...
Other AbstractPrincipal component analysis (PCA) is widely used in industrial process monitoring. PCA has drawbacks when dealing with non-Gaussian process data and the covariance matrix it uses can only evaluate the linear relationship among variables and ignore the nonlinear dependence information. To solve this shortcoming, a novel process monitoring method based on logarithmic transformation and maximal information coefficient-PCA is proposed. Firstly, logarithmic transformation is used to transform the process data to improve the data distribution in a certain degree. Then, the covariance matrix can be replaced by the MIC matrix which can measure the non-linear correlation between the variables, so as to improve the monitoring effect on nonlinear and non-Gaussian process. Finally, the feasibility and effectiveness of the proposed method are verified by the Tennessee Eastman (TE) process simulation.
Language中文
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/20743
Collection数字工厂研究室
Corresponding Author李帅
Affiliation1.中国科学院大学
2.中国科学院沈阳自动化研究所
3.中国科学院网络化控制系统重点实验室
Recommended Citation
GB/T 7714
王中伟,宋宏,李帅,等. 基于对数变换和最大信息系数PCA的过程监测[J]. 科学技术与工程,2017,17(16):259-265.
APA 王中伟,宋宏,李帅,&周晓锋.(2017).基于对数变换和最大信息系数PCA的过程监测.科学技术与工程,17(16),259-265.
MLA 王中伟,et al."基于对数变换和最大信息系数PCA的过程监测".科学技术与工程 17.16(2017):259-265.
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