Improved Principal Component Monitoring of Large-Scale Processes

Uwe Kruger; George Irwin; Y. Zhou

doi:10.1016/j.jprocont.2004.02.002

Improved Principal Component Monitoring of Large-Scale Processes

Uwe Kruger, George Irwin, Y. Zhou

Research output: Contribution to journal › Article › peer-review

68 Citations (Scopus)

Abstract

This is the first paper that shows and theoretically analyses that the presence of auto-correlation can produce considerable alterations in the Type I and Type II errors in univariate and multivariate statistical control charts. To remove this undesired effect, linear inverse ARMA filter are employed and the application studies in this paper show that false alarms (increased Type I errors) and an insensitive monitoring statistics (increased Type II errors) were eliminated.

Original language	English
Pages (from-to)	879-888
Number of pages	10
Journal	Journal of Process Control
Volume	14
Issue number	8
DOIs	https://doi.org/10.1016/j.jprocont.2004.02.002
Publication status	Published - Mar 2004

ASJC Scopus subject areas

Process Chemistry and Technology
Control and Systems Engineering
Industrial and Manufacturing Engineering

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10.1016/j.jprocont.2004.02.002

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title = "Improved Principal Component Monitoring of Large-Scale Processes",

abstract = "This is the first paper that shows and theoretically analyses that the presence of auto-correlation can produce considerable alterations in the Type I and Type II errors in univariate and multivariate statistical control charts. To remove this undesired effect, linear inverse ARMA filter are employed and the application studies in this paper show that false alarms (increased Type I errors) and an insensitive monitoring statistics (increased Type II errors) were eliminated.",

author = "Uwe Kruger and George Irwin and Y. Zhou",

year = "2004",

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AU - Irwin, George

AU - Zhou, Y.

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AB - This is the first paper that shows and theoretically analyses that the presence of auto-correlation can produce considerable alterations in the Type I and Type II errors in univariate and multivariate statistical control charts. To remove this undesired effect, linear inverse ARMA filter are employed and the application studies in this paper show that false alarms (increased Type I errors) and an insensitive monitoring statistics (increased Type II errors) were eliminated.

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DO - 10.1016/j.jprocont.2004.02.002

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EP - 888

JO - Journal of Process Control

JF - Journal of Process Control

SN - 0959-1524

IS - 8

ER -

Improved Principal Component Monitoring of Large-Scale Processes

Abstract

ASJC Scopus subject areas

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