Nonparametric multivariate control charts based on a linkage ranking algorithm

Helen Meyers Bush, Panitarn Chongfuangprinya, V. C P Chen, Thuntee Sukchotrat, Seoung Bum Kim

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

Control charts have been widely recognized as important and critical tools in system monitoring for detection of abnormal behavior and quality improvement. In particular, multivariate control charts have been effectively used when a process involves a number of correlated quality characteristics. Most existing multivariate control charts were developed using the assumption of normally distributed quality characteristics. However, process data from modern industries often do not follow the normal distribution. Despite the great need for nonparametric control charts that can control the error rate regardless of the underlying distribution, few efforts have been made in this direction. In this paper, we propose a new nonparametric control chart (called the kLINK chart) based on a k-linkage ranking algorithm that calculates the ranking of a new observation relative to the in-control training data. A simulation study was performed to demonstrate the effectiveness of our kLINK chart and its superiority over the traditional Hotelling's T2 chart and the ranking depth control chart in nonnormal situations. In addition, to enable increased sensitivity to small shifts, we present an exponentially weighted moving average version of a kLINK chart.

Original languageEnglish
Pages (from-to)663-675
Number of pages13
JournalQuality and Reliability Engineering International
Volume26
Issue number7
DOIs
Publication statusPublished - 2010 Nov 1

Fingerprint

Normal distribution
Control charts
Multivariate control charts
Charts
Ranking
Linkage
Monitoring
Quality characteristics
Industry
Simulation study
System monitoring
Quality improvement
Exponentially weighted moving average
Hotelling

Keywords

  • data depth
  • Hotelling's T
  • multivariate control charts
  • nonparametric
  • statistical quality control

ASJC Scopus subject areas

  • Management Science and Operations Research
  • Safety, Risk, Reliability and Quality

Cite this

Nonparametric multivariate control charts based on a linkage ranking algorithm. / Bush, Helen Meyers; Chongfuangprinya, Panitarn; Chen, V. C P; Sukchotrat, Thuntee; Kim, Seoung Bum.

In: Quality and Reliability Engineering International, Vol. 26, No. 7, 01.11.2010, p. 663-675.

Research output: Contribution to journalArticle

Bush, Helen Meyers ; Chongfuangprinya, Panitarn ; Chen, V. C P ; Sukchotrat, Thuntee ; Kim, Seoung Bum. / Nonparametric multivariate control charts based on a linkage ranking algorithm. In: Quality and Reliability Engineering International. 2010 ; Vol. 26, No. 7. pp. 663-675.
@article{aa964c94ed8a4f5b96bdaa46ae33c768,
title = "Nonparametric multivariate control charts based on a linkage ranking algorithm",
abstract = "Control charts have been widely recognized as important and critical tools in system monitoring for detection of abnormal behavior and quality improvement. In particular, multivariate control charts have been effectively used when a process involves a number of correlated quality characteristics. Most existing multivariate control charts were developed using the assumption of normally distributed quality characteristics. However, process data from modern industries often do not follow the normal distribution. Despite the great need for nonparametric control charts that can control the error rate regardless of the underlying distribution, few efforts have been made in this direction. In this paper, we propose a new nonparametric control chart (called the kLINK chart) based on a k-linkage ranking algorithm that calculates the ranking of a new observation relative to the in-control training data. A simulation study was performed to demonstrate the effectiveness of our kLINK chart and its superiority over the traditional Hotelling's T2 chart and the ranking depth control chart in nonnormal situations. In addition, to enable increased sensitivity to small shifts, we present an exponentially weighted moving average version of a kLINK chart.",
keywords = "data depth, Hotelling's T, multivariate control charts, nonparametric, statistical quality control",
author = "Bush, {Helen Meyers} and Panitarn Chongfuangprinya and Chen, {V. C P} and Thuntee Sukchotrat and Kim, {Seoung Bum}",
year = "2010",
month = "11",
day = "1",
doi = "10.1002/qre.1129",
language = "English",
volume = "26",
pages = "663--675",
journal = "Quality and Reliability Engineering International",
issn = "0748-8017",
publisher = "John Wiley and Sons Ltd",
number = "7",

}

TY - JOUR

T1 - Nonparametric multivariate control charts based on a linkage ranking algorithm

AU - Bush, Helen Meyers

AU - Chongfuangprinya, Panitarn

AU - Chen, V. C P

AU - Sukchotrat, Thuntee

AU - Kim, Seoung Bum

PY - 2010/11/1

Y1 - 2010/11/1

N2 - Control charts have been widely recognized as important and critical tools in system monitoring for detection of abnormal behavior and quality improvement. In particular, multivariate control charts have been effectively used when a process involves a number of correlated quality characteristics. Most existing multivariate control charts were developed using the assumption of normally distributed quality characteristics. However, process data from modern industries often do not follow the normal distribution. Despite the great need for nonparametric control charts that can control the error rate regardless of the underlying distribution, few efforts have been made in this direction. In this paper, we propose a new nonparametric control chart (called the kLINK chart) based on a k-linkage ranking algorithm that calculates the ranking of a new observation relative to the in-control training data. A simulation study was performed to demonstrate the effectiveness of our kLINK chart and its superiority over the traditional Hotelling's T2 chart and the ranking depth control chart in nonnormal situations. In addition, to enable increased sensitivity to small shifts, we present an exponentially weighted moving average version of a kLINK chart.

AB - Control charts have been widely recognized as important and critical tools in system monitoring for detection of abnormal behavior and quality improvement. In particular, multivariate control charts have been effectively used when a process involves a number of correlated quality characteristics. Most existing multivariate control charts were developed using the assumption of normally distributed quality characteristics. However, process data from modern industries often do not follow the normal distribution. Despite the great need for nonparametric control charts that can control the error rate regardless of the underlying distribution, few efforts have been made in this direction. In this paper, we propose a new nonparametric control chart (called the kLINK chart) based on a k-linkage ranking algorithm that calculates the ranking of a new observation relative to the in-control training data. A simulation study was performed to demonstrate the effectiveness of our kLINK chart and its superiority over the traditional Hotelling's T2 chart and the ranking depth control chart in nonnormal situations. In addition, to enable increased sensitivity to small shifts, we present an exponentially weighted moving average version of a kLINK chart.

KW - data depth

KW - Hotelling's T

KW - multivariate control charts

KW - nonparametric

KW - statistical quality control

UR - http://www.scopus.com/inward/record.url?scp=78349267918&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78349267918&partnerID=8YFLogxK

U2 - 10.1002/qre.1129

DO - 10.1002/qre.1129

M3 - Article

AN - SCOPUS:78349267918

VL - 26

SP - 663

EP - 675

JO - Quality and Reliability Engineering International

JF - Quality and Reliability Engineering International

SN - 0748-8017

IS - 7

ER -