基于数据特性分析的多变量过程监测

doi:10.12068/j.issn.1005-3026.2017.05.001

东北大学学报:自然科学版 ›› 2017, Vol. 38 ›› Issue (5): 609-614.DOI: 10.12068/j.issn.1005-3026.2017.05.001

• 信息与控制 • 下一篇

基于数据特性分析的多变量过程监测

张淑美¹，王福利^1,2，王姝^1,2，李嫱嫱³

(1. 东北大学信息科学与工程学院，辽宁沈阳110819; 2. 东北大学流程工业综合自动化国家重点实验室，辽宁沈阳110819; 3. 沈阳航空航天大学设计艺术学院，辽宁沈阳110136)

收稿日期:2015-12-17 修回日期:2015-12-17 出版日期:2017-05-15 发布日期:2017-05-11
通讯作者: 张淑美
作者简介:张淑美(1988-)，女，河北保定人，东北大学博士研究生; 王福利(1957-)，男，辽宁沈阳人，东北大学教授，博士生导师.
基金资助:
国家自然科学基金资助项目(61374146)；辽宁省科学技术计划项目(2015020051)；中央高校基本科研业务费专项资金资助项目(N140404020).

Multivariate Process Monitoring Based on the Characteristic Analysis of the Data

ZHANG Shu-mei¹， WANG Fu-li^1,2， WANG Shu^1,2， LI Qiang-qiang³

1. School of Information Science & Engineering， Northeastern University， Shenyang 110819， China; 2. State Key Laboratory of Synthetical Automation for Process Industries， Northeastern University， Shenyang 110819， China; 3. College of Design & Art， Shenyang Aerospace University， Shenyang 110136， China.

Received:2015-12-17 Revised:2015-12-17 Online:2017-05-15 Published:2017-05-11
Contact: WANG Shu
About author:-
Supported by:
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摘要/Abstract

摘要：

以PCA，ICA为代表的多元统计监测方法总是基于各种各样的前提假设，如果不考虑它们的适用条件盲目选择监测算法，则可能给出错误结论，增加故障误报漏报的概率.针对理论方法在应用时面临的条件限制问题，在无先验知识的情况下，提出一种数据特性的分析方法，通过参数寻优并逐步剔除线性相关变量组的方法，实现多变量过程线性非线性的自动判别.仿真分析表明所提方法可以根据数据特点及各算法的适用条件自动选择适当的监测算法，具有一定的实用价值.

Abstract:

Multivariate statistical process monitoring methods such as PCA and ICA are always based on a variety of assumptions. If the constraints of these methods have not been considered when selecting these methods， wrong conclusions will be obtained and the rate of high leaking and false alarm will be increased. To solve the constraints problem of these methods in application， a data characteristic analysis method was proposed to test the correlation of the variables automatically， in which parameter optimization was conducted and the set of linear variables was eliminated sequentially. The simulation illustrated that the proposed method can select appropriate modelling method automatically according to data characteristics and applicable conditions of the methods， which has considerable practical value.

Key words: correlation of the variables, principal component analysis, independent component analysis, kernel principal component analysis, kernel independent component analysis

中图分类号:

TP277

张淑美，王福利，王姝，李嫱嫱. 基于数据特性分析的多变量过程监测[J]. 东北大学学报:自然科学版, 2017, 38(5): 609-614.

ZHANG Shu-mei， WANG Fu-li， WANG Shu， LI Qiang-qiang. Multivariate Process Monitoring Based on the Characteristic Analysis of the Data[J]. Journal of Northeastern University:Natural Science, 2017, 38(5): 609-614.

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基于数据特性分析的多变量过程监测

Multivariate Process Monitoring Based on the Characteristic Analysis of the Data

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