Interpreting the Out-of-Control Signals of Multivariate Control Charts Employing Neural Networks

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For example, in the case of control charts designed to monitor the mean vector, the chart signals showing that it must be accepted that there is a shift in the vector, but no indication is given about the variables that have produced this shift. The MEWMA quality control chart is a very powerful scheme to detect small shifts in the mean vector. There are no previous specific works about the interpretation of the out-of-control signal of this chart. In this paper neural networks are designed to interpret the out-of-control signal of the MEWMA chart, and the percentage of correct classifications is studied for different cases." /> <meta name="citation_title" content="Interpreting the Out-of-Control Signals of Multivariate Control Charts Employing Neural Networks" /> <meta name="citation_doi" content="10.5281/zenodo.1071568" /> <meta name="citation_keywords" content="Multivariate quality control" /> <meta name="citation_keywords" content="Artificial Intelligence" /> <meta name="citation_keywords" content="Neural Networks" /> <meta name="citation_keywords" content="Computer Applications" /> <meta name="citation_abstract_html_url" content="https://zenodo.org/records/1071568" /> <meta property="og:title" content="Interpreting the Out-of-Control Signals of Multivariate Control Charts Employing Neural Networks" /> <meta property="og:description" content="Multivariate quality control charts show some advantages to monitor several variables in comparison with the simultaneous use of univariate charts, nevertheless, there are some disadvantages. The main problem is how to interpret the out-ofcontrol signal of a multivariate chart. For example, in the case of control charts designed to monitor the mean vector, the chart signals showing that it must be accepted that there is a shift in the vector, but no indication is given about the variables that have produced this shift. The MEWMA quality control chart is a very powerful scheme to detect small shifts in the mean vector. There are no previous specific works about the interpretation of the out-of-control signal of this chart. In this paper neural networks are designed to interpret the out-of-control signal of the MEWMA chart, and the percentage of correct classifications is studied for different cases." /> <meta property="og:url" content="https://zenodo.org/records/1071568" /> <meta property="og:site_name" content="Zenodo" /> <meta name="twitter:card" content="summary" /> <meta name="twitter:site" content="@zenodo_org" /> <meta name="twitter:title" content="Interpreting the Out-of-Control Signals of Multivariate Control Charts Employing Neural Networks" /> <meta name="twitter:description" content="Multivariate quality control charts show some advantages to monitor several variables in comparison with the simultaneous use of univariate charts, nevertheless, there are some disadvantages. The main problem is how to interpret the out-ofcontrol signal of a multivariate chart. For example, in the case of control charts designed to monitor the mean vector, the chart signals showing that it must be accepted that there is a shift in the vector, but no indication is given about the variables that have produced this shift. The MEWMA quality control chart is a very powerful scheme to detect small shifts in the mean vector. There are no previous specific works about the interpretation of the out-of-control signal of this chart. In this paper neural networks are designed to interpret the out-of-control signal of the MEWMA chart, and the percentage of correct classifications is studied for different cases." /> <meta name="citation_pdf_url" content="https://zenodo.org/records/1071568/files/8798.pdf"/> <link rel="alternate" type="application/pdf" href="https://zenodo.org/records/1071568/files/8798.pdf"> <link rel="canonical" href="https://zenodo.org/records/1071568"> <title>Interpreting the Out-of-Control Signals of Multivariate Control Charts Employing Neural Networks</title> <link rel="shortcut icon" type="image/x-icon" href="/static/favicon.ico"/> <link rel="apple-touch-icon" sizes="120x120" href="/static/apple-touch-icon-120.png"/> <link rel="apple-touch-icon" sizes="152x152" href="/static/apple-touch-icon-152.png"/> <link rel="apple-touch-icon" sizes="167x167" href="/static/apple-touch-icon-167.png"/> <link rel="apple-touch-icon" sizes="180x180" href="/static/apple-touch-icon-180.png"/> <link 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container"> <div class="ui relaxed grid mt-5"> <div class="two column row top-padded"> <article class="sixteen wide tablet eleven wide computer column main-record-content"> <section id="record-info" aria-label="Publication date and version number"> <div class="ui grid middle aligned"> <div class="two column row"> <div class="left floated left aligned column"> <span class="ui" title="Publication date"> Published January 26, 2010 </span> <span class="label text-muted"> | Version 8798</span> </div> <div class="right floated right aligned column"> <span role="note" class="ui label horizontal small neutral mb-5" aria-label="Resource type" > Journal article </span> <span role="note" class="ui label horizontal small access-status open mb-5" data-tooltip="The record and files are publicly accessible." data-inverted="" aria-label="Access status" > <i class="icon unlock" aria-hidden="true"></i> <span aria-label="The record and files are publicly accessible."> Open </span> </span> </div> </div> </div> </section> <div class="ui divider hidden"></div><section id="record-title-section" aria-label="Record title and creators"> <h1 id="record-title" class="wrap-overflowing-text">Interpreting the Out-of-Control Signals of Multivariate Control Charts Employing Neural Networks</h1> <section id="creatibutors" aria-label="Creators and contributors"> <div class="ui grid"> <div class="row ui accordion affiliations"> <div class="sixteen wide mobile twelve wide tablet thirteen wide computer column"> <h3 class="sr-only">Creators</h3> <ul class="creatibutors"> <li class="creatibutor-wrap separated"> <a class="ui creatibutor-link" href="/search?q=metadata.creators.person_or_org.name%3A%22Francisco+Aparisi%22" > <span class="creatibutor-name">Francisco Aparisi</span></a> </li> <li class="creatibutor-wrap separated"> <a class="ui creatibutor-link" href="/search?q=metadata.creators.person_or_org.name%3A%22Jos%C3%A9+Sanz%22" > <span class="creatibutor-name">Jos茅 Sanz</span></a> </li> </ul> </div> </div> </div> </section> </section> <section id="description" class="rel-mt-2 rich-input-content" aria-label="Record description"> <h2 id="description-heading" class="sr-only">Description</h2> <div style="word-wrap: break-word;"> <p><p>Multivariate quality control charts show some advantages to monitor several variables in comparison with the simultaneous use of univariate charts, nevertheless, there are some disadvantages. The main problem is how to interpret the out-ofcontrol signal of a multivariate chart. For example, in the case of control charts designed to monitor the mean vector, the chart signals showing that it must be accepted that there is a shift in the vector, but no indication is given about the variables that have produced this shift. The MEWMA quality control chart is a very powerful scheme to detect small shifts in the mean vector. There are no previous specific works about the interpretation of the out-of-control signal of this chart. In this paper neural networks are designed to interpret the out-of-control signal of the MEWMA chart, and the percentage of correct classifications is studied for different cases.</p></p> </div> </section> <section id="record-files" class="rel-mt-2 rel-mb-3" aria-label="Files" ><h2 id="files-heading">Files</h2> <div class="ui accordion panel mb-10 open" href="#files-preview-accordion-panel"> <h3 class="active title panel-heading open m-0"> <div role="button" id="files-preview-accordion-trigger" aria-controls="files-preview-accordion-panel" aria-expanded="true" tabindex="0" class="trigger" aria-label="File preview" > <span id="preview-file-title">8798.pdf</span> <i class="angle right icon" aria-hidden="true"></i> </div> </h3> <div role="region" id="files-preview-accordion-panel" aria-labelledby="files-preview-accordion-trigger" class="active content preview-container pt-0 open" > <div> <iframe title="Preview" class="preview-iframe" id="preview-iframe" name="preview-iframe" src="/records/1071568/preview/8798.pdf?include_deleted=0" > </iframe> </div> </div> </div> <div class="ui accordion panel mb-10 open" href="#files-list-accordion-panel"> <h3 class="active title panel-heading open m-0"> <div role="button" id="files-list-accordion-trigger" aria-controls="files-list-accordion-panel" aria-expanded="true" tabindex="0" class="trigger"> Files <small class="text-muted"> (379.4 kB)</small> <i class="angle right icon" aria-hidden="true"></i> </div> </h3> <div role="region" id="files-list-accordion-panel" aria-labelledby="files-list-accordion-trigger" class="active content pt-0"> <div> <table class="ui striped table files fluid open"> <thead> <tr> <th>Name</th> <th>Size</th> <th class> <a role="button" class="ui compact mini button right floated archive-link" href="https://zenodo.org/api/records/1071568/files-archive"> <i class="file archive icon button" aria-hidden="true"></i> Download all </a> </th> </tr> </thead> <tbody> <tr> <td class="ten wide"> <div> <a href="/records/1071568/files/8798.pdf?download=1">8798.pdf</a> </div> <small class="ui text-muted font-tiny">md5:9f137a2db6ad259a0cb4493da74c5264 <div class="ui icon inline-block" data-tooltip="This is the file fingerprint (checksum), which can be used to verify the file integrity."> <i class="question circle checksum icon"></i> </div> </small> </td> <td>379.4 kB</td> <td class="right aligned"> <span> <a role="button" class="ui compact mini button preview-link" href="/records/1071568/preview/8798.pdf?include_deleted=0" target="preview-iframe" data-file-key="8798.pdf"> <i class="eye icon" aria-hidden="true"></i>Preview </a> <a role="button" class="ui compact mini button" href="/records/1071568/files/8798.pdf?download=1"> <i class="download icon" aria-hidden="true"></i>Download </a> </span> </td> </tr> </tbody> </table> </div> </div> </div> </section> <section id="additional-details" class="rel-mt-2" aria-label="Additional record details"> <h2 id="record-details-heading">Additional details</h2> <div class="ui divider"></div> <div class="ui fluid accordion padded grid rel-mb-1"> <div class="active title sixteen wide mobile four wide tablet three wide computer column"> <h3 class="ui header"> <div id="references-accordion-trigger" role="button" tabindex="0" aria-expanded="true" aria-controls="references-panel" class="trigger" > <i class="caret right icon" aria-hidden="true"></i>References </div> </h3> </div> <div id="references-panel" role="region" aria-labelledby="references-accordion-trigger" class="active content sixteen wide mobile twelve wide tablet thirteen wide computer column" > <ul class="ui bulleted list details-list"> <li class="item">Lowry, C.A., Woodall, W.H., Champ, C.W., and Rigdon, S.E. A multivariate exponentially weighted moving average control chart. Technometrics, 1992, 34 (1), 46-53.</li> <li class="item">Prabhu, SS. And Runger, GC. Designing a multivariate EWMA control chart. Journal of Quality Technology, 1997, 29:8-15.</li> <li class="item">Roberts, S. W. (1959). Control Chart Test Based on Geometrics Moving Averages. Technometrics, 1959, 1, pp. 239-250.</li> <li class="item">Montgomery, D.C. Introduction to Statistical Quality Control .4rd. John Wiley. New York. 2001</li> <li class="item">Lowry CA. and, Montgomery, DC. A review of multivariate control charts. IIE Transactions, 1995; 27: 800-810.</li> <li class="item">Blazek, L.W., Novic B .and Scott M.D. Displaying Multivariate Data Using Polyplots. Journal of Quality Technology, 1987, 19(2), 69-74.</li> <li class="item">Subramanyan, N. and Houshmand, A.A. Simultaneous representation of multivariate and corresponding univariate charts using line graph. Quality Engineering, 1995, 4, 681-682.</li> <li class="item">Fuchs, C. and Benjamin, Y. Multivariate profile charts for statistical process control. Technometrics, 1994, 36(2), 182-195.</li> <li class="item">Iglewicz, B. and Hoaglin, D.C. Use of boxplots for process evaluation. Journal of Quality Technology, 1987, 19(4), 180-190. [10] Atienza, O.O., Ching L.T. and Wah, B.A. Simultaneous monitoring of univariante and multivariate SPC information using boxplots. International Journal of Quality Science, 1988, 3(2). [11] Doganaksoy, N., Faltin, F.W. and Tucker, W.T. Identification of-out-of control characteristics in a multivariate manufacturing environment. Communications in Statistics Theory and Methods, 1991, 20, 2775- 2790. [12] Runger, G.C., Alt, F.B., and Montgomery, D.C. Contributors to a multivariate statistical process control chart signal. Communications in Statistics. Theory Methods, 1996, 25, 2203-2213. [13] Mason, R. L., Tracy, N.D. and Young, J.C. Decomposition of T2 multivariate control chart interpretation. Journal of Quality Technology, 1995, 27(2), 99-108. [14] Mason, R.L., Tracy, N.D. and Young, J.C. A practical approach for interpreting multivariate T2 control chart signals. Journal of Quality Technology, 1997, 29(4), 396-406. [15] Aparisi, F., Avenda鈹溾枓o, G. and Sanz, J. Interpreting T2 Control Chart Signals: Effectiveness of MTY decomposition vs. a Neural Network, IIE Transactions, 2006, 38(8), 647-657.</li> </ul> </div> </div> <div class="ui divider"></div> </section> <section id="citations-search" data-record-pids='{"doi": {"client": "datacite", "identifier": "10.5281/zenodo.1071568", "provider": "datacite"}, "oai": {"identifier": "oai:zenodo.org:1071568", "provider": "oai"}}' data-record-parent-pids='{"doi": {"client": "datacite", "identifier": "10.5281/zenodo.1071567", "provider": "datacite"}}' data-citations-endpoint="https://zenodo-broker.web.cern.ch/api/relationships" aria-label="Record citations" class="rel-mb-1" > </section> </article> <aside class="sixteen wide tablet five wide computer column sidebar" aria-label="Record details"> <section id="metrics" aria-label="Metrics" class="ui segment rdm-sidebar sidebar-container"> <div class="ui tiny two statistics rel-mt-1"> <div class="ui statistic"> <div class="value">59</div> <div class="label"> 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A\nmultivariate exponentially weighted moving average control chart.\nTechnometrics, 1992, 34 (1), 46-53."}, {"reference": "Prabhu, SS. And Runger, GC. Designing a multivariate EWMA control\nchart. Journal of Quality Technology, 1997, 29:8-15."}, {"reference": "Roberts, S. W. (1959). Control Chart Test Based on Geometrics Moving\nAverages. Technometrics, 1959, 1, pp. 239-250."}, {"reference": "Montgomery, D.C. Introduction to Statistical Quality Control .4rd. John\nWiley. New York. 2001"}, {"reference": "Lowry CA. and, Montgomery, DC. A review of multivariate control\ncharts. IIE Transactions, 1995; 27: 800-810."}, {"reference": "Blazek, L.W., Novic B .and Scott M.D. Displaying Multivariate Data\nUsing Polyplots. Journal of Quality Technology, 1987, 19(2), 69-74."}, {"reference": "Subramanyan, N. and Houshmand, A.A. Simultaneous representation of\nmultivariate and corresponding univariate charts using line graph.\nQuality Engineering, 1995, 4, 681-682."}, {"reference": "Fuchs, C. and Benjamin, Y. Multivariate profile charts for statistical\nprocess control. Technometrics, 1994, 36(2), 182-195."}, {"reference": "Iglewicz, B. and Hoaglin, D.C. Use of boxplots for process evaluation.\nJournal of Quality Technology, 1987, 19(4), 180-190.\n[10] Atienza, O.O., Ching L.T. and Wah, B.A. Simultaneous monitoring of\nunivariante and multivariate SPC information using boxplots.\nInternational Journal of Quality Science, 1988, 3(2).\n[11] Doganaksoy, N., Faltin, F.W. and Tucker, W.T. Identification of-out-of\ncontrol characteristics in a multivariate manufacturing environment.\nCommunications in Statistics Theory and Methods, 1991, 20, 2775-\n2790.\n[12] Runger, G.C., Alt, F.B., and Montgomery, D.C. Contributors to a\nmultivariate statistical process control chart signal. Communications in\nStatistics. Theory Methods, 1996, 25, 2203-2213.\n[13] Mason, R. L., Tracy, N.D. and Young, J.C. Decomposition of T2\nmultivariate control chart interpretation. Journal of Quality Technology,\n1995, 27(2), 99-108.\n[14] Mason, R.L., Tracy, N.D. and Young, J.C. A practical approach for\ninterpreting multivariate T2 control chart signals. Journal of Quality\nTechnology, 1997, 29(4), 396-406.\n[15] Aparisi, F., Avenda\u251c\u2592o, G. and Sanz, J. 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Simultaneous representation of\nmultivariate and corresponding univariate charts using line graph.\nQuality Engineering, 1995, 4, 681-682."}, {"reference": "Fuchs, C. and Benjamin, Y. Multivariate profile charts for statistical\nprocess control. Technometrics, 1994, 36(2), 182-195."}, {"reference": "Iglewicz, B. and Hoaglin, D.C. Use of boxplots for process evaluation.\nJournal of Quality Technology, 1987, 19(4), 180-190.\n[10] Atienza, O.O., Ching L.T. and Wah, B.A. Simultaneous monitoring of\nunivariante and multivariate SPC information using boxplots.\nInternational Journal of Quality Science, 1988, 3(2).\n[11] Doganaksoy, N., Faltin, F.W. and Tucker, W.T. Identification of-out-of\ncontrol characteristics in a multivariate manufacturing environment.\nCommunications in Statistics Theory and Methods, 1991, 20, 2775-\n2790.\n[12] Runger, G.C., Alt, F.B., and Montgomery, D.C. Contributors to a\nmultivariate statistical process control chart signal. Communications in\nStatistics. 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The main problem is how to interpret the out-ofcontrol signal of a multivariate chart. For example, in the case of control charts designed to monitor the mean vector, the chart signals showing that it must be accepted that there is a shift in the vector, but no indication is given about the variables that have produced this shift. The MEWMA quality control chart is a very powerful scheme to detect small shifts in the mean vector. There are no previous specific works about the interpretation of the out-of-control signal of this chart. In this paper neural networks are designed to interpret the out-of-control signal of the MEWMA chart, and the percentage of correct classifications is studied for different cases.\u003c/p\u003e", "identifier": "https://doi.org/10.5281/zenodo.1071568", "inLanguage": {"@type": "Language", "alternateName": "eng", "name": "English"}, "keywords": "Multivariate quality control, Artificial Intelligence, Neural Networks, Computer Applications", "license": "https://creativecommons.org/licenses/by/4.0/legalcode", "name": "Interpreting the Out-of-Control Signals of Multivariate Control Charts Employing Neural Networks", "publisher": {"@type": "Organization", "name": "Zenodo"}, "size": "370.48 KB", "url": "https://zenodo.org/records/1071568", "version": "8798"}</script> <script src="/static/dist/js/invenio-app-rdm-landing-page-theme.9b56690388e335810f04.js"></script> <script src="/static/dist/js/9945.e11a5a6ff50535c72070.js"></script> <script src="/static/dist/js/1357.4e237807ffba81b213b0.js"></script> <script src="/static/dist/js/1644.2b2007bc83e4beeabfaf.js"></script> <script src="/static/dist/js/8962.cfabe841decd009221fd.js"></script> <script src="/static/dist/js/9300.a81535ba51a38f1472fe.js"></script> <script src="/static/dist/js/9693.dac033d778162b60d96f.js"></script> <script src="/static/dist/js/invenio-app-rdm-landing-page.024f3c02bb324ddef007.js"></script> <script src="/static/dist/js/previewer_theme.77f20174699c7786038a.js"></script> <script src="/static/dist/js/zenodo-rdm-citations.f6ca22bc7712ee9b03f7.js"></script> <div class="ui container info message cookie-banner hidden"> <i class="close icon"></i> <div> <i aria-hidden="true" class="info icon"></i> <p class="inline">This site uses cookies. 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Interpreting the Out-of-Control Signals of Multivariate Control Charts Employing Neural Networks