On the effect of measurement errors in regression-adjusted monitoring of multistage manufacturing processes

Guoliang Ding; Li Zeng

doi:10.1016/j.jmsy.2014.06.013

On the effect of measurement errors in regression-adjusted monitoring of multistage manufacturing processes

Guoliang Ding
, Li Zeng^*

^*Corresponding author for this work

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

11 Citations (Scopus)

Abstract

A critical challenge in multistage process monitoring is the complex relationships between quality characteristics at different stages. A popular method to deal with this problem is regression adjustment in which each quality characteristic is regressed on its preceding quality characteristics and the resulting residual is monitored to detect changes in local variations. However, the performance of this method depends on the accuracy of the regression coefficient estimation. One source of the estimation errors is measurement errors which commonly exist in practice. To provide guidance on the use of regression-adjusted monitoring methods, this study investigates the effect of measurement errors on the bias of regression estimation theoretically and numerically. Two estimators, the ordinary least squares (OLS) estimator and the total least squares (TLS) estimator, are compared, and insights regarding their performance are obtained.

Original language	English
Article number	311
Pages (from-to)	263-273
Journal	Journal of Manufacturing Systems
Volume	36
DOIs	https://doi.org/10.1016/j.jmsy.2014.06.013
Publication status	Published - 2015
Externally published	Yes

Bibliographical note

Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 9 Industry, Innovation, and Infrastructure

Research Keywords

Measurement errors
Multistage manufacturing process (MMP)
Ordinary least squares (OLS)
Regression adjustment
Total least squares (TLS)

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10.1016/j.jmsy.2014.06.013

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title = "On the effect of measurement errors in regression-adjusted monitoring of multistage manufacturing processes",

abstract = "A critical challenge in multistage process monitoring is the complex relationships between quality characteristics at different stages. A popular method to deal with this problem is regression adjustment in which each quality characteristic is regressed on its preceding quality characteristics and the resulting residual is monitored to detect changes in local variations. However, the performance of this method depends on the accuracy of the regression coefficient estimation. One source of the estimation errors is measurement errors which commonly exist in practice. To provide guidance on the use of regression-adjusted monitoring methods, this study investigates the effect of measurement errors on the bias of regression estimation theoretically and numerically. Two estimators, the ordinary least squares (OLS) estimator and the total least squares (TLS) estimator, are compared, and insights regarding their performance are obtained.",

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author = "Guoliang Ding and Li Zeng",

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T1 - On the effect of measurement errors in regression-adjusted monitoring of multistage manufacturing processes

AU - Ding, Guoliang

AU - Zeng, Li

N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].

PY - 2015

Y1 - 2015

N2 - A critical challenge in multistage process monitoring is the complex relationships between quality characteristics at different stages. A popular method to deal with this problem is regression adjustment in which each quality characteristic is regressed on its preceding quality characteristics and the resulting residual is monitored to detect changes in local variations. However, the performance of this method depends on the accuracy of the regression coefficient estimation. One source of the estimation errors is measurement errors which commonly exist in practice. To provide guidance on the use of regression-adjusted monitoring methods, this study investigates the effect of measurement errors on the bias of regression estimation theoretically and numerically. Two estimators, the ordinary least squares (OLS) estimator and the total least squares (TLS) estimator, are compared, and insights regarding their performance are obtained.

AB - A critical challenge in multistage process monitoring is the complex relationships between quality characteristics at different stages. A popular method to deal with this problem is regression adjustment in which each quality characteristic is regressed on its preceding quality characteristics and the resulting residual is monitored to detect changes in local variations. However, the performance of this method depends on the accuracy of the regression coefficient estimation. One source of the estimation errors is measurement errors which commonly exist in practice. To provide guidance on the use of regression-adjusted monitoring methods, this study investigates the effect of measurement errors on the bias of regression estimation theoretically and numerically. Two estimators, the ordinary least squares (OLS) estimator and the total least squares (TLS) estimator, are compared, and insights regarding their performance are obtained.

KW - Measurement errors

KW - Multistage manufacturing process (MMP)

KW - Ordinary least squares (OLS)

KW - Regression adjustment

KW - Total least squares (TLS)

UR - https://www.scopus.com/pages/publications/84942293663

UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-84942293663&origin=recordpage

U2 - 10.1016/j.jmsy.2014.06.013

DO - 10.1016/j.jmsy.2014.06.013

M3 - RGC 21 - Publication in refereed journal

SN - 0278-6125

VL - 36

SP - 263

EP - 273

JO - Journal of Manufacturing Systems

JF - Journal of Manufacturing Systems

M1 - 311

ER -

On the effect of measurement errors in regression-adjusted monitoring of multistage manufacturing processes

Abstract

Bibliographical note

UN SDGs

Research Keywords

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