SPC Monitoring of MMSE- and Pi-Controlled Processes

To reduce variation in manufacturing processes, traditional statistical process control (SPC) techniques can be applied to monitor automatic process control (APC) controlled processes for detecting assignable cause process variation. In this paper we compare the monitoring of process output and the...

Full description

Saved in:
Bibliographic Details
Published in:Journal of quality technology 2002-10, Vol.34 (4), p.384-398
Main Authors: Jiang, Wei, Tsui, Kwok-Leung
Format: Article
Language:English
Subjects:
Autoregressive Moving Average Process
Comparative analysis
Control charts
Controllers
Noise
Process controls
Quality Control
Ratios
Regression analysis
Statistical Process Control
Studies
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c370t-5af477e5981c07574fff196f6c7e44f182975dd5ba92c61c54863bb9ad5876623
cites cdi_FETCH-LOGICAL-c370t-5af477e5981c07574fff196f6c7e44f182975dd5ba92c61c54863bb9ad5876623
container_end_page 398
container_issue 4
container_start_page 384
container_title Journal of quality technology
container_volume 34
creator Jiang, Wei
Tsui, Kwok-Leung
description To reduce variation in manufacturing processes, traditional statistical process control (SPC) techniques can be applied to monitor automatic process control (APC) controlled processes for detecting assignable cause process variation. In this paper we compare the monitoring of process output and the monitoring of the control action of Minimum-Mean-Squared-Error- and Proportional-Integral-Controlled processes. We show that the robustness property of the PI controller makes it difficult to detect unanticipated mean shifts when the process output is being monitored. We illustrate how the signal-to-noise ratios developed in Jiang, Tsui, and Woodall (2000) can be used to predict the SPC chart performance and help select the appropriate chart for monitoring.
doi_str_mv 10.1080/00224065.2002.11980171
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1080_00224065_2002_11980171</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>209112471</sourcerecordid><originalsourceid>FETCH-LOGICAL-c370t-5af477e5981c07574fff196f6c7e44f182975dd5ba92c61c54863bb9ad5876623</originalsourceid><addsrcrecordid>eNqFkE1LAzEQhoMoWKt_QRYvnrZOvrPHUuoHWCxUzyHNJrJlm9Rki_Tfu0vtxYunmYHnfRkehG4xTDAoeAAghIHgE9JvE4wrBVjiMzQiXPJSEArnaDRA5UBdoqucNwCYgGIjRFfLWbGIoeliasJnEX2xWKzmZWFCXSybchZDl2Lbuv5K0bqcXb5GF9602d38zjH6eJy_z57L17enl9n0tbRUQldy45mUjlcKW5BcMu89roQXVjrGPFakkryu-dpUxApsOVOCrteVqbmSov97jO6PvbsUv_Yud3rbZOva1gQX91lLRpmgHGhP3v0hN3GfQv-cJpgxRZRgPSSOkE0x5-S83qVma9JBY9CDSX0yqQeT-mSyD06PwSb4mLbmO6a21p05tDH5ZIJtsqb_dPwARmV2zA</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>214482864</pqid></control><display><type>article</type><title>SPC Monitoring of MMSE- and Pi-Controlled Processes</title><source>ABI/INFORM Collection</source><source>Taylor and Francis Science and Technology Collection</source><creator>Jiang, Wei ; Tsui, Kwok-Leung</creator><creatorcontrib>Jiang, Wei ; Tsui, Kwok-Leung</creatorcontrib><description>To reduce variation in manufacturing processes, traditional statistical process control (SPC) techniques can be applied to monitor automatic process control (APC) controlled processes for detecting assignable cause process variation. In this paper we compare the monitoring of process output and the monitoring of the control action of Minimum-Mean-Squared-Error- and Proportional-Integral-Controlled processes. We show that the robustness property of the PI controller makes it difficult to detect unanticipated mean shifts when the process output is being monitored. We illustrate how the signal-to-noise ratios developed in Jiang, Tsui, and Woodall (2000) can be used to predict the SPC chart performance and help select the appropriate chart for monitoring.</description><identifier>ISSN: 0022-4065</identifier><identifier>EISSN: 2575-6230</identifier><identifier>DOI: 10.1080/00224065.2002.11980171</identifier><identifier>CODEN: JQUTAU</identifier><language>eng</language><publisher>Milwaukee: Taylor &amp; Francis</publisher><subject>Autoregressive Moving Average Process ; Comparative analysis ; Control charts ; Controllers ; Noise ; Process controls ; Quality Control ; Ratios ; Regression analysis ; Statistical Process Control ; Studies</subject><ispartof>Journal of quality technology, 2002-10, Vol.34 (4), p.384-398</ispartof><rights>Copyright 2002 ASQ 2002</rights><rights>Copyright American Society for Quality Oct 2002</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c370t-5af477e5981c07574fff196f6c7e44f182975dd5ba92c61c54863bb9ad5876623</citedby><cites>FETCH-LOGICAL-c370t-5af477e5981c07574fff196f6c7e44f182975dd5ba92c61c54863bb9ad5876623</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/214482864/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$H</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/214482864?pq-origsite=primo$$EHTML$$P50$$Gproquest$$H</linktohtml><link.rule.ids>314,780,784,11688,27924,27925,36060,36061,44363,74767</link.rule.ids></links><search><creatorcontrib>Jiang, Wei</creatorcontrib><creatorcontrib>Tsui, Kwok-Leung</creatorcontrib><title>SPC Monitoring of MMSE- and Pi-Controlled Processes</title><title>Journal of quality technology</title><description>To reduce variation in manufacturing processes, traditional statistical process control (SPC) techniques can be applied to monitor automatic process control (APC) controlled processes for detecting assignable cause process variation. In this paper we compare the monitoring of process output and the monitoring of the control action of Minimum-Mean-Squared-Error- and Proportional-Integral-Controlled processes. We show that the robustness property of the PI controller makes it difficult to detect unanticipated mean shifts when the process output is being monitored. We illustrate how the signal-to-noise ratios developed in Jiang, Tsui, and Woodall (2000) can be used to predict the SPC chart performance and help select the appropriate chart for monitoring.</description><subject>Autoregressive Moving Average Process</subject><subject>Comparative analysis</subject><subject>Control charts</subject><subject>Controllers</subject><subject>Noise</subject><subject>Process controls</subject><subject>Quality Control</subject><subject>Ratios</subject><subject>Regression analysis</subject><subject>Statistical Process Control</subject><subject>Studies</subject><issn>0022-4065</issn><issn>2575-6230</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2002</creationdate><recordtype>article</recordtype><sourceid>M0C</sourceid><recordid>eNqFkE1LAzEQhoMoWKt_QRYvnrZOvrPHUuoHWCxUzyHNJrJlm9Rki_Tfu0vtxYunmYHnfRkehG4xTDAoeAAghIHgE9JvE4wrBVjiMzQiXPJSEArnaDRA5UBdoqucNwCYgGIjRFfLWbGIoeliasJnEX2xWKzmZWFCXSybchZDl2Lbuv5K0bqcXb5GF9602d38zjH6eJy_z57L17enl9n0tbRUQldy45mUjlcKW5BcMu89roQXVjrGPFakkryu-dpUxApsOVOCrteVqbmSov97jO6PvbsUv_Yud3rbZOva1gQX91lLRpmgHGhP3v0hN3GfQv-cJpgxRZRgPSSOkE0x5-S83qVma9JBY9CDSX0yqQeT-mSyD06PwSb4mLbmO6a21p05tDH5ZIJtsqb_dPwARmV2zA</recordid><startdate>20021001</startdate><enddate>20021001</enddate><creator>Jiang, Wei</creator><creator>Tsui, Kwok-Leung</creator><general>Taylor &amp; Francis</general><general>Taylor &amp; Francis Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>0U~</scope><scope>1-H</scope><scope>3V.</scope><scope>7TB</scope><scope>7WY</scope><scope>7WZ</scope><scope>7XB</scope><scope>87Z</scope><scope>88I</scope><scope>8AO</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>8FL</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BEZIV</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FRNLG</scope><scope>F~G</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>K60</scope><scope>K6~</scope><scope>L.-</scope><scope>L.0</scope><scope>L6V</scope><scope>M0C</scope><scope>M2P</scope><scope>M7S</scope><scope>PQBIZ</scope><scope>PQBZA</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PTHSS</scope><scope>PYYUZ</scope><scope>Q9U</scope><scope>S0X</scope></search><sort><creationdate>20021001</creationdate><title>SPC Monitoring of MMSE- and Pi-Controlled Processes</title><author>Jiang, Wei ; Tsui, Kwok-Leung</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c370t-5af477e5981c07574fff196f6c7e44f182975dd5ba92c61c54863bb9ad5876623</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2002</creationdate><topic>Autoregressive Moving Average Process</topic><topic>Comparative analysis</topic><topic>Control charts</topic><topic>Controllers</topic><topic>Noise</topic><topic>Process controls</topic><topic>Quality Control</topic><topic>Ratios</topic><topic>Regression analysis</topic><topic>Statistical Process Control</topic><topic>Studies</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jiang, Wei</creatorcontrib><creatorcontrib>Tsui, Kwok-Leung</creatorcontrib><collection>CrossRef</collection><collection>Global News &amp; ABI/Inform Professional</collection><collection>Trade PRO</collection><collection>ProQuest Central (Corporate)</collection><collection>Mechanical &amp; Transportation Engineering Abstracts</collection><collection>ABI/INFORM Collection</collection><collection>ABI/INFORM Global (PDF only)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection</collection><collection>Science Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ABI/INFORM Collection (Alumni Edition)</collection><collection>Materials Science &amp; Engineering Collection</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest Business Premium Collection</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central</collection><collection>Engineering Research Database</collection><collection>Business Premium Collection (Alumni)</collection><collection>ABI/INFORM Global (Corporate)</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection (Proquest) (PQ_SDU_P3)</collection><collection>ProQuest Business Collection (Alumni Edition)</collection><collection>ProQuest Business Collection</collection><collection>ABI/INFORM Professional Advanced</collection><collection>ABI/INFORM Professional Standard</collection><collection>ProQuest Engineering Collection</collection><collection>ABI/INFORM Collection</collection><collection>ProQuest Science Journals</collection><collection>Engineering Database</collection><collection>One Business (ProQuest)</collection><collection>ProQuest One Business (Alumni)</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Engineering Collection</collection><collection>ABI/INFORM Collection China</collection><collection>ProQuest Central Basic</collection><collection>SIRS Editorial</collection><jtitle>Journal of quality technology</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jiang, Wei</au><au>Tsui, Kwok-Leung</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>SPC Monitoring of MMSE- and Pi-Controlled Processes</atitle><jtitle>Journal of quality technology</jtitle><date>2002-10-01</date><risdate>2002</risdate><volume>34</volume><issue>4</issue><spage>384</spage><epage>398</epage><pages>384-398</pages><issn>0022-4065</issn><eissn>2575-6230</eissn><coden>JQUTAU</coden><abstract>To reduce variation in manufacturing processes, traditional statistical process control (SPC) techniques can be applied to monitor automatic process control (APC) controlled processes for detecting assignable cause process variation. In this paper we compare the monitoring of process output and the monitoring of the control action of Minimum-Mean-Squared-Error- and Proportional-Integral-Controlled processes. We show that the robustness property of the PI controller makes it difficult to detect unanticipated mean shifts when the process output is being monitored. We illustrate how the signal-to-noise ratios developed in Jiang, Tsui, and Woodall (2000) can be used to predict the SPC chart performance and help select the appropriate chart for monitoring.</abstract><cop>Milwaukee</cop><pub>Taylor &amp; Francis</pub><doi>10.1080/00224065.2002.11980171</doi><tpages>15</tpages></addata></record>
fulltext fulltext
identifier ISSN: 0022-4065
ispartof Journal of quality technology, 2002-10, Vol.34 (4), p.384-398
issn 0022-4065
2575-6230
language eng
recordid cdi_crossref_primary_10_1080_00224065_2002_11980171
source ABI/INFORM Collection; Taylor and Francis Science and Technology Collection
subjects Autoregressive Moving Average Process
Comparative analysis
Control charts
Controllers
Noise
Process controls
Quality Control
Ratios
Regression analysis
Statistical Process Control
Studies
title SPC Monitoring of MMSE- and Pi-Controlled Processes
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-07T21%3A16%3A03IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=SPC%20Monitoring%20of%20MMSE-%20and%20Pi-Controlled%20Processes&rft.jtitle=Journal%20of%20quality%20technology&rft.au=Jiang,%20Wei&rft.date=2002-10-01&rft.volume=34&rft.issue=4&rft.spage=384&rft.epage=398&rft.pages=384-398&rft.issn=0022-4065&rft.eissn=2575-6230&rft.coden=JQUTAU&rft_id=info:doi/10.1080/00224065.2002.11980171&rft_dat=%3Cproquest_cross%3E209112471%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c370t-5af477e5981c07574fff196f6c7e44f182975dd5ba92c61c54863bb9ad5876623%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=214482864&rft_id=info:pmid/&rfr_iscdi=true