Review of Multivariate Statistical Process Monitoring

被引：0
|
作者：
Xie, Xiang [1 ]
Shi, Hongbo [1 ]
Yang, Wen [1 ]
机构：
[1] E China Univ Sci & Technol, Dept Automat, Shanghai 200237, Peoples R China
来源：
2010 8TH WORLD CONGRESS ON INTELLIGENT CONTROL AND AUTOMATION (WCICA) | 2010年
关键词：
statistical process monitoring; continuous process; batch process; fault detection; PRINCIPAL COMPONENT ANALYSIS; BATCH PROCESSES; FAULT-DETECTION; MISSING DATA; PCA; CLASSIFICATION; DIAGNOSIS; SPACE; STRATEGY;
D O I：
10.1109/WCICA.2010.5553941
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
A comprehensive literature survey of multivariate statistical process monitoring methods of recent years is presented. Principle component analysis based methods are reviewed according to their emphases on either data attributes, such as missing value, outliers, nonlinear, time-varying, serial correlation, non-Gaussian distribution and multi-scale, or operational attributes such as multi-block, multi-mode, transition process, multi-stage. All the methods mentioned in this survey can be extended to other statistical models easily.
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页码：4201 / 4208
页数：8
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