{"id":5253,"date":"2023-03-14T12:53:31","date_gmt":"2023-03-14T11:53:31","guid":{"rendered":"https:\/\/datascience.unifi.it\/?post_type=tribe_events&p=5253"},"modified":"2023-03-14T12:53:31","modified_gmt":"2023-03-14T11:53:31","slug":"disia-steering-itenbis-seminar","status":"publish","type":"tribe_events","link":"https:\/\/datascience.unifi.it\/index.php\/event\/disia-steering-itenbis-seminar\/","title":{"rendered":"DiSIA – StEering – itENBIS Seminar"},"content":{"rendered":"

The Department of Statistics, Computer Science, Applications DiSIA is happy to invite you to a seminar organized with the StEering<\/a> – Statistics for Engineering: design, quality and reliability and itENBIS<\/a> the Italian group of the European Network for Business and Industrial Statistics<\/p>\n

Speaker: G. Geoffrey Vining (Virginia Tech, USA)<\/p>\n

Title: Shewhart and Profile Monitoring for Industry 4.0<\/span><\/p>\n

Abstract:<\/p>\n

An important current area within statistical process control\/monitoring is profile monitoring, which assumes that the underlying profile of the data over time is some linear, nonlinear, or nonparametric model.\u00a0 Let\u00a0y<\/em>\u00a0be the characteristic of interest, and let\u00a0f(y;\u00a0<\/em>\u03b8, x<\/strong>)<\/em>\u00a0\u00a0be the underlying model, where\u00a0x<\/strong><\/em>\u00a0is the\u00a0p\u00a0<\/em>x\u00a01<\/em>\u00a0vector of variables that explain the behavior of\u00a0y<\/strong>\u00a0over time\u00a0and\u00a0<\/strong>\u03b8<\/strong><\/em>\u00a0is an unknown vector of model parameters relating\u00a0\u00a0to\u00a0y<\/strong>.\u00a0 The standard approach taken by the profile monitoring community uses the following algorithm:<\/p>\n

(1)\u00a0\u00a0Estimate\u00a0\u03b8<\/strong><\/em>\u00a0for each individual value of\u00a0y<\/em>.\u00a0 Let $\\hat{\\theta}_{i}$\u00a0be the resulting vector of estimates associated with each individual $y_{i}$.\u00a0 Let\u00a0$\\hat{\\theta}_{avg}$\u00a0be the average value of the\u00a0$\\hat{\\theta}_{i}$s.<\/p>\n

(2)\u00a0\u00a0Estimate the variance of\u00a0$\\hat{\\theta}_{i}$\u00a0by computing estimates of every variance and covariance involving the components of\u00a0x<\/strong><\/em>.<\/p>\n

(3)\u00a0\u00a0Construct control limits using some variation of Hotelling\u2019s $T^{2}$\u00a0statistic.<\/p>\n

\n

This approach historically assumes that\u00a0p<\/em>\u00a0is very small.\u00a0 There are many serious issues from a linear-models perspective to this approach, not the least of which is an unnecessary need to estimate $p+\\binom{p}{2}$\u00a0variance components, which typically require much larger sample sizes to estimate than averages.<\/p>\n

Industry 4.0 increases the number of sensors that can provide huge amounts of information in real time.\u00a0 As a result, there are opportunities to align data on those variables known to impact a critical quality characteristic to improve the monitoring of that characteristic.\u00a0 The case study that underlies this talk has very good information on 40 variables known to impact the performance of the critical quality characteristic.\u00a0 The current profile monitoring approach requires the estimation of 780 variances\/covariances, which is completely unrealistic.<\/p>\n

This talk outlines how to incorporate the extra information efficiently and effectively.\u00a0 Ironically, this approach has its origins in Shewhart\u2019s original ideas underlying control charts.\u00a0 Seeing the connection is important for advances in statistical process monitoring within Industry 4.0.<\/p>\n

The seminar will be held in DiSIA ‘s meeting room 205, and also online.<\/p>\n

In order to get the link for the webinar you should register by 20 March at noon – sending an email to centro.steering@disia.unifi.it with the subject: “Webinar-Vining”.<\/p>\n","protected":false},"excerpt":{"rendered":"

The Department of Statistics, Computer Science, Applications DiSIA is happy to invite you to a seminar organized with the StEering – Statistics for Engineering: design, quality and reliability and itENBIS …<\/p>\n","protected":false},"author":12,"featured_media":0,"template":"","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"_tribe_events_status":"","_tribe_events_status_reason":"","footnotes":""},"tags":[],"tribe_events_cat":[35],"class_list":["post-5253","tribe_events","type-tribe_events","status-publish","hentry","tribe_events_cat-seminar","cat_seminar"],"_links":{"self":[{"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/tribe_events\/5253","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/tribe_events"}],"about":[{"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/types\/tribe_events"}],"author":[{"embeddable":true,"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/users\/12"}],"version-history":[{"count":1,"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/tribe_events\/5253\/revisions"}],"predecessor-version":[{"id":5254,"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/tribe_events\/5253\/revisions\/5254"}],"wp:attachment":[{"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/media?parent=5253"}],"wp:term":[{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/tags?post=5253"},{"taxonomy":"tribe_events_cat","embeddable":true,"href":"https:\/\/datascience.unifi.it\/index.php\/wp-json\/wp\/v2\/tribe_events_cat?post=5253"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}