Multivariate statistical monitoring of process operating performance
作者:
James V. Kresta,
John F. Macgregor,
Thomas E. Marlin,
期刊:
The Canadian Journal of Chemical Engineering
(WILEY Available online 1991)
卷期:
Volume 69,
issue 1
页码: 35-47
ISSN:0008-4034
年代: 1991
DOI:10.1002/cjce.5450690105
出版商: Wiley Subscription Services, Inc., A Wiley Company
关键词: statistical process control;partial least squares;projection to latent structures;principal component analysis;performance monitoring;fault detection
数据来源: WILEY
摘要:
AbstractProcess computers routinely collect hundreds to thousands of pieces of data from a multitude of plant sensors every few seconds. This has caused a “data overload” and due to the lack of appropriate analyses very little is currently being done to utilize this wealth of information. Operating personnel typically use only a few variables to monitor the plant's performance. However, multivariate statistical methods such as PLS (Partial Least Squares or Projection to Latent Structures) and PCA (Principal Component Analysis) are capable of compressing the information down into low dimensional spaces which retain most of the information. Using this method of statistical data compression a multivariate monitoring procedure analogous to the univariate Shewart Chart has been developed to efficiently monitor the performance of large processes, and to rapidly detect and identify important process changes. This procedure is demonstrated using simulations of two processes, a fluidized bed reactor and an extractive distillation col
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