{"id":5772,"date":"2022-07-13T15:23:38","date_gmt":"2022-07-13T14:23:38","guid":{"rendered":"https:\/\/www.axpe.com\/?p=5772"},"modified":"2025-04-09T10:51:42","modified_gmt":"2025-04-09T09:51:42","slug":"el-cluster-data-ia-se-involucra-con-mantenimiento-predictivo-en-industria","status":"publish","type":"post","link":"https:\/\/www.axpe.com\/en\/el-cluster-data-ia-se-involucra-con-mantenimiento-predictivo-en-industria\/","title":{"rendered":"The AXPE Data and AI Cluster is dedicated to Predictive Maintenance for the Industry."},"content":{"rendered":"<div data-elementor-type=\"wp-post\" data-elementor-id=\"5772\" class=\"elementor elementor-5772\">\n\t\t\t\t<div class=\"elementor-element elementor-element-28cc7644 e-flex e-con-boxed e-con e-parent\" data-id=\"28cc7644\" data-element_type=\"container\" data-e-type=\"container\">\n\t\t\t\t\t<div class=\"e-con-inner\">\n\t\t\t\t<div class=\"elementor-element elementor-element-775537b elementor-widget elementor-widget-spacer\" data-id=\"775537b\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-023d4ad news-header elementor-widget elementor-widget-heading\" data-id=\"023d4ad\" data-element_type=\"widget\" data-e-type=\"widget\" data-no-translation=\"\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<h2 class=\"elementor-heading-title elementor-size-default\">The Data &amp; AI Cluster Engages in Predictive Maintenance for Industry<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-f6e3cc8 news-header elementor-widget elementor-widget-heading\" data-id=\"f6e3cc8\" data-element_type=\"widget\" data-e-type=\"widget\" data-no-translation=\"\" data-widget_type=\"heading.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<style>.elementor-element-f6e3cc8{display:none !important}<\/style>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-214755b1 elementor-widget elementor-widget-text-editor\" data-id=\"214755b1\" data-element_type=\"widget\" data-e-type=\"widget\" data-no-translation=\"\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t\t\t\n<p class=\"wp-block-paragraph\"><strong>Most industrial companies<\/strong> with production lines in logistics plants <strong>lack real-time insights into the health of their machinery<\/strong>. They rely on maintenance sessions\u2014akin to small blood samples\u2014to estimate a machine\u2019s likelihood of critical failure or to determine if a failure has already occurred. This approach is known as preventive maintenance.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\"><strong>Preventive maintenance sessions<\/strong> are scheduled conservatively, meaning they occur frequently to reduce the risk of critical production failures that could lead to significant financial losses. However, this <strong>high frequency<\/strong> also results in additional costs that could be optimized with better management.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">Another <strong>approach is condition-based maintenance<\/strong>, where maintenance is performed when an issue is detected. However, <strong>by the time an error is identified<\/strong>, it is often <strong>too late <\/strong>or leaves little room for correction, leading to rushed responses that can, in turn, cause future failures.<\/p>\n\n\n\n<figure class=\"wp-block-image size-large\"><img fetchpriority=\"high\" decoding=\"async\" width=\"1024\" height=\"385\" class=\"wp-image-5774\" src=\"https:\/\/www2.axpe.com\/wp-content\/uploads\/2022\/07\/image001-1024x385.png\" alt=\"\" srcset=\"https:\/\/www.axpe.com\/wp-content\/uploads\/2022\/07\/image001-1024x385.png 1024w, https:\/\/www.axpe.com\/wp-content\/uploads\/2022\/07\/image001-600x226.png 600w, https:\/\/www.axpe.com\/wp-content\/uploads\/2022\/07\/image001-300x113.png 300w, https:\/\/www.axpe.com\/wp-content\/uploads\/2022\/07\/image001-768x289.png 768w, https:\/\/www.axpe.com\/wp-content\/uploads\/2022\/07\/image001-1536x578.png 1536w, https:\/\/www.axpe.com\/wp-content\/uploads\/2022\/07\/image001-1080x406.png 1080w, https:\/\/www.axpe.com\/wp-content\/uploads\/2022\/07\/image001.png 1600w\" sizes=\"(max-width: 1024px) 100vw, 1024px\" \/><\/figure>\n\n\n\n<p class=\"wp-block-paragraph\">The <strong>predictive maintenance model<\/strong> combines the strengths of preventive and condition-based maintenance by leveraging <strong>artificial intelligence<\/strong> to <strong>forecast<\/strong> when <strong>maintenance tasks will be needed<\/strong> well in advance. This approach ensures optimal machine performance while reducing unnecessary maintenance sessions, leading to <strong>cost optimization<\/strong>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The <strong>Data &amp; AI Cluster&#8217;s proposed solution <\/strong>focuses on identifying <strong>key metrics that can effectively model machine health<\/strong>. Once these metrics are established, the next step involves <strong>deploying sensors to collect real-time<\/strong> <strong>data<\/strong> and <strong>developing a predictive maintenance model<\/strong>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">The system is based on a <strong>functional architecture<\/strong> where sensors continuously <strong>gather machine data<\/strong> and <strong>store it in an IoT infrastructure<\/strong>. The collected data undergoes processing before being sent to a database that maintains historical records, including past machine errors. The processed data is also<strong> displayed on a dashboard for real-time monitoring<\/strong>.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">An<strong> AI model is then trained to analyze historical data and its correlation with past failures,<\/strong> allowing it to predict future errors days, weeks, or even months in advance. These predictions can also be stored in the database and visualized on the dashboard for proactive decision-making.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">A key contributor to the technological development of this project is <strong>Alejandro Ramos<\/strong>, a member of the Data &amp; AI Cluster, under the leadership of <strong>Gema Atienza<\/strong>. The team remains committed to delivering advanced tools that enhance efficiency across the industrial sector.<\/p>\n\n\n\n<p class=\"wp-block-paragraph\">&nbsp;<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-b256561 elementor-widget elementor-widget-text-editor\" data-id=\"b256561\" data-element_type=\"widget\" data-e-type=\"widget\" data-no-translation=\"\" data-widget_type=\"text-editor.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t<style>.elementor-element-b256561{display:none !important}<\/style>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<div class=\"elementor-element elementor-element-ab98afc elementor-widget elementor-widget-spacer\" data-id=\"ab98afc\" data-element_type=\"widget\" data-e-type=\"widget\" data-widget_type=\"spacer.default\">\n\t\t\t\t<div class=\"elementor-widget-container\">\n\t\t\t\t\t\t\t<div class=\"elementor-spacer\">\n\t\t\t<div class=\"elementor-spacer-inner\"><\/div>\n\t\t<\/div>\n\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t<\/div>","protected":false},"excerpt":{"rendered":"<p>Most industrial companies with production lines in logistics plants lack real-time insights into the health of their machinery.<\/p>","protected":false},"author":1,"featured_media":13685,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[22,23],"tags":[62,61,63],"class_list":["post-5772","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-press-room","category-analysis-and-trends","tag-advance-analytics-ai","tag-cloud-devops","tag-modern-apps-api"],"_links":{"self":[{"href":"https:\/\/www.axpe.com\/en\/wp-json\/wp\/v2\/posts\/5772","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.axpe.com\/en\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.axpe.com\/en\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.axpe.com\/en\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.axpe.com\/en\/wp-json\/wp\/v2\/comments?post=5772"}],"version-history":[{"count":38,"href":"https:\/\/www.axpe.com\/en\/wp-json\/wp\/v2\/posts\/5772\/revisions"}],"predecessor-version":[{"id":17775,"href":"https:\/\/www.axpe.com\/en\/wp-json\/wp\/v2\/posts\/5772\/revisions\/17775"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/www.axpe.com\/en\/wp-json\/wp\/v2\/media\/13685"}],"wp:attachment":[{"href":"https:\/\/www.axpe.com\/en\/wp-json\/wp\/v2\/media?parent=5772"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.axpe.com\/en\/wp-json\/wp\/v2\/categories?post=5772"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.axpe.com\/en\/wp-json\/wp\/v2\/tags?post=5772"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}