{"id":2904,"date":"2026-06-23T10:51:07","date_gmt":"2026-06-23T10:51:07","guid":{"rendered":"https:\/\/artelnics.com\/case_studies\/celsa-rolling-mill-predictive-maintenance\/"},"modified":"2026-06-23T11:22:09","modified_gmt":"2026-06-23T11:22:09","slug":"celsa-rolling-mill-predictive-maintenance","status":"publish","type":"case_studies","link":"https:\/\/artelnics.com\/case_studies\/celsa-rolling-mill-predictive-maintenance\/","title":{"rendered":"Predictive maintenance in a rolling mill"},"content":{"rendered":"<style>\n.atl-case{font-family:Roboto,Arial,sans-serif;color:#17384d;line-height:1.68;max-width:980px;margin:0 auto;padding:20px 18px 44px}\n.atl-case h2{font-family:Outfit,Arial,sans-serif;color:#17384d;margin:34px 0 10px;font-size:30px;line-height:1.2}\n.atl-case p{font-size:18px;margin:0 0 16px}\n.atl-case .lead{font-size:22px;color:#244f68;margin:6px 0 26px}\n.atl-case .meta{display:flex;flex-wrap:wrap;gap:10px;margin:0 0 28px}\n.atl-case .tag{background:#e9f5fa;color:#1e5374;border:1px solid #cbe7f2;border-radius:999px;padding:7px 12px;font-weight:700;font-size:14px}\n.atl-case .grid{display:grid;grid-template-columns:repeat(2,minmax(0,1fr));gap:18px;margin:24px 0}\n.atl-case .card{background:#f7fafc;border:1px solid #dbe8ee;border-radius:8px;padding:18px}\n.atl-case .card strong{display:block;font-family:Outfit,Arial,sans-serif;font-size:18px;color:#1e5374;margin-bottom:6px}\n.atl-case figure{margin:28px 0;text-align:center}\n.atl-case img{max-width:100%;height:auto;border-radius:8px;border:1px solid #dbe8ee}\n.atl-case figcaption{font-size:14px;color:#5f7583;margin-top:8px}\n@media(max-width:760px){.atl-case .grid{grid-template-columns:1fr}.atl-case h2{font-size:25px}.atl-case p{font-size:16px}.atl-case .lead{font-size:19px}}\n<\/style>\n<article class=\"atl-case\">\n<p class=\"lead\">Health indicators and anomaly detection models help detect abnormal behavior in rolling equipment before it turns into downtime.<\/p>\n<div class=\"meta\">\n    <span class=\"tag\">Steel manufacturing<\/span><br \/>\n    <span class=\"tag\">Anomaly detection<\/span><br \/>\n    <span class=\"tag\">Predictive maintenance<\/span>\n  <\/div>\n<h2>Challenge<\/h2>\n<p>Rolling mills generate many process signals across campaigns, products and routes. Maintenance teams need to know when equipment behavior deviates from normal operation.<\/p>\n<h2>Data<\/h2>\n<p>Historical campaigns were grouped by product and route, using process variables such as speed, torque, tension, RPM and load indicators.<\/p>\n<h2>Solution<\/h2>\n<p>Artelnics combined statistical ranges with auto-associative neural networks to calculate health indicators and detect deviations in real time.<\/p>\n<div class=\"grid\">\n<div class=\"card\"><strong>Method<\/strong><\/p>\n<p>Auto-associative neural networks and statistical baselines.<\/p>\n<\/div>\n<div class=\"card\"><strong>Output<\/strong><\/p>\n<p>Deviation indicators and alarms by equipment context.<\/p>\n<\/div>\n<\/div>\n<h2>Results<\/h2>\n<p>The solution supports proactive maintenance by highlighting when process behavior moves outside expected operating patterns.<\/p>\n<h2>Illustrations<\/h2>\n<figure><img decoding=\"async\" src=\"https:\/\/artelnics.com\/wp-content\/uploads\/2026\/06\/celsa_maintenance_dev.jpg\" alt=\"Deviation indicators for rolling mill operation\"><figcaption>Rolling mill deviation chart<\/figcaption><\/figure>\n<\/article>\n","protected":false},"excerpt":{"rendered":"<p>Health indicators and anomaly detection models help detect abnormal behavior in rolling equipment before it turns into downtime.<\/p>\n","protected":false},"featured_media":2915,"parent":0,"menu_order":0,"template":"","categories":[],"class_list":["post-2904","case_studies","type-case_studies","status-publish","has-post-thumbnail","hentry"],"acf":[],"_links":{"self":[{"href":"https:\/\/artelnics.com\/api\/wp\/v2\/case_studies\/2904","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/artelnics.com\/api\/wp\/v2\/case_studies"}],"about":[{"href":"https:\/\/artelnics.com\/api\/wp\/v2\/types\/case_studies"}],"version-history":[{"count":1,"href":"https:\/\/artelnics.com\/api\/wp\/v2\/case_studies\/2904\/revisions"}],"predecessor-version":[{"id":2916,"href":"https:\/\/artelnics.com\/api\/wp\/v2\/case_studies\/2904\/revisions\/2916"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/artelnics.com\/api\/wp\/v2\/media\/2915"}],"wp:attachment":[{"href":"https:\/\/artelnics.com\/api\/wp\/v2\/media?parent=2904"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/artelnics.com\/api\/wp\/v2\/categories?post=2904"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}