摘要:

Rationales for process monitoring using some of the techniques of statistical process control and for feedback adjustment using some techniques associated with automatic process control are explored, and issues that sometimes arise are discussed. The importance of some often unstated assumptions are illustrated. Minimum-cost feedback schemes are discussed for some simple, but practically interesting, models.

ISSN:0040-1706    

DOI:10.1080/00401706.1992.10485271

出版商:Taylor & Francis Group    

年代:1992

数据来源: Taylor

ISSN:0040-1706    

DOI:10.1080/00401706.1992.10485272

出版商:Taylor & Francis Group    

年代:1992

数据来源: Taylor

ISSN:0040-1706    

DOI:10.1080/00401706.1992.10485273

出版商:Taylor & Francis Group    

年代:1992

数据来源: Taylor

ISSN:0040-1706    

DOI:10.1080/00401706.1992.10485274

出版商:Taylor & Francis Group    

年代:1992

数据来源: Taylor

ISSN:0040-1706    

DOI:10.1080/00401706.1992.10485275

出版商:Taylor & Francis Group    

年代:1992

数据来源: Taylor

ISSN:0040-1706    

DOI:10.1080/00401706.1992.10485276

出版商:Taylor & Francis Group    

年代:1992

数据来源: Taylor

ISSN:0040-1706    

DOI:10.1080/00401706.1992.10485277

出版商:Taylor & Francis Group    

年代:1992

数据来源: Taylor

摘要:

The goal of algorithmic statistical process control is to reduce predictable quality variations using feedback and feedforward techniques and then monitor the complete system to detect and remove unexpected root causes of variation. This methodology seeks to exploit the strengths of both automatic control and statistical process control (SPC), two fields that have developed in relative isolation from one another. Recent experience with the control and monitoring of intrinsic viscosity from a particular General Electric polymerization process has led to a better understanding of how SPC and feedback control can be united into a single system. Building on past work by MacGregor, Box, Astrom, and others, the article covers the application from statistical identification and modeling to implementing feedback control and final SPC monitoring. Operational and technical issues that arose are examined, and a general approach is outlined.

ISSN:0040-1706    

DOI:10.1080/00401706.1992.10485278

出版商:Taylor & Francis Group    

年代:1992

数据来源: Taylor

摘要:

Minimum-variance controllers provide an essential link for integrating concepts of statistical process control and engineering process control. These controllers are specifically designed to minimize a quadratic loss function. In some problems, however, other loss functions may be more appropriate. In this article, controllers that are optimal for nonsymmetric and nonquadratic loss functions are derived. In many cases, these controllers retain the basic simplicity of the minimum-variance controller. Additionally, for process disturbances driven by normal white noise, the minimum-variance controller is shown to be optimal for a broad class of objective functions.

ISSN:0040-1706    

DOI:10.1080/00401706.1992.10485279

出版商:Taylor & Francis Group    

年代:1992

数据来源: Taylor

摘要:

The boxplot has proven to be a very useful tool for summarizing univariate data. Several options of bivariate boxplot-type constructions are discussed. These include both elliptic and asymmetric plots. An inner region contains 50% of the data, and a fence identifies potential outliers. Such a robust plot shows location, scale, correlation, and a resistant regression line. Alternative constructions are compared in terms of efficiency of the relevant parameters. Additional properties are given and recommendations made. Emphasis is given to the bivariate biweightMestimator. Several practical examples illustrate that standard least squares ellipsoids can give graphically misleading summaries.

ISSN:0040-1706    

DOI:10.1080/00401706.1992.10485280

出版商:Taylor & Francis Group    

年代:1992

数据来源: Taylor