{"version":"1.0","provider_name":"Revista Nuclear Espa\u00f1a","provider_url":"https:\/\/www.revistanuclear.es\/en\/","title":"Machine learning methods in equipment life management - Safety","type":"rich","width":600,"height":338,"html":"<blockquote class=\"wp-embedded-content\" data-secret=\"PAsvXmfj8q\"><a href=\"https:\/\/www.revistanuclear.es\/en\/safety\/machine-learning-methods-in-equipment-life-management\/\">Machine learning methods in equipment life management<\/a><\/blockquote><iframe sandbox=\"allow-scripts\" security=\"restricted\" src=\"https:\/\/www.revistanuclear.es\/en\/safety\/machine-learning-methods-in-equipment-life-management\/embed\/#?secret=PAsvXmfj8q\" width=\"600\" height=\"338\" title=\"&#8220;Machine learning methods in equipment life management&#8221; &#8212; Revista Nuclear Espa\u00f1a\" data-secret=\"PAsvXmfj8q\" frameborder=\"0\" marginwidth=\"0\" marginheight=\"0\" scrolling=\"no\" class=\"wp-embedded-content\"><\/iframe><script>\n\/*! This file is auto-generated *\/\n!function(d,l){\"use strict\";l.querySelector&&d.addEventListener&&\"undefined\"!=typeof URL&&(d.wp=d.wp||{},d.wp.receiveEmbedMessage||(d.wp.receiveEmbedMessage=function(e){var t=e.data;if((t||t.secret||t.message||t.value)&&!\/[^a-zA-Z0-9]\/.test(t.secret)){for(var s,r,n,a=l.querySelectorAll('iframe[data-secret=\"'+t.secret+'\"]'),o=l.querySelectorAll('blockquote[data-secret=\"'+t.secret+'\"]'),c=new RegExp(\"^https?:$\",\"i\"),i=0;i<o.length;i++)o[i].style.display=\"none\";for(i=0;i<a.length;i++)s=a[i],e.source===s.contentWindow&&(s.removeAttribute(\"style\"),\"height\"===t.message?(1e3<(r=parseInt(t.value,10))?r=1e3:~~r<200&&(r=200),s.height=r):\"link\"===t.message&&(r=new URL(s.getAttribute(\"src\")),n=new URL(t.value),c.test(n.protocol))&&n.host===r.host&&l.activeElement===s&&(d.top.location.href=t.value))}},d.addEventListener(\"message\",d.wp.receiveEmbedMessage,!1),l.addEventListener(\"DOMContentLoaded\",function(){for(var e,t,s=l.querySelectorAll(\"iframe.wp-embedded-content\"),r=0;r<s.length;r++)(t=(e=s[r]).getAttribute(\"data-secret\"))||(t=Math.random().toString(36).substring(2,12),e.src+=\"#?secret=\"+t,e.setAttribute(\"data-secret\",t)),e.contentWindow.postMessage({message:\"ready\",secret:t},\"*\")},!1)))}(window,document);\n\/\/# sourceURL=https:\/\/www.revistanuclear.es\/wp-includes\/js\/wp-embed.min.js\n<\/script>\n","thumbnail_url":"https:\/\/www.revistanuclear.es\/wp-content\/uploads\/2026\/02\/Gestion-equipos.jpg","thumbnail_width":1920,"thumbnail_height":1080,"description":"panish nuclear power plants have accumulated extensive operational experience, approaching 40 years of service, and must continuously maintain the highest standards of safety and reliability in their systems. In this context, the development of tools capable of accurately assessing equipment health and predicting Remaining Useful Life (RUL) is of great value. By leveraging information generated through online condition monitoring, degradation can be detected and failures prevented, thereby ensuring safe and efficient plant operation. Proposed methodology This study presents the methodology [&hellip;]"}