{"id":32714,"date":"2024-05-10T09:39:05","date_gmt":"2024-05-10T09:39:05","guid":{"rendered":"https:\/\/new.teeptrak.com\/machine-learning\/"},"modified":"2026-06-18T04:08:57","modified_gmt":"2026-06-18T04:08:57","slug":"machine-learning-deteccion-anomalias","status":"publish","type":"page","link":"https:\/\/teeptrak.com\/es\/machine-learning-deteccion-anomalias\/","title":{"rendered":"Machine Learning"},"content":{"rendered":"<p>[et_pb_section fb_built=\u00bb1&#8243; admin_label=\u00bbTemplate B\u00bb _builder_version=\u00bb4.27.4&#8243; background_color=\u00bb#FFFFFF\u00bb custom_padding=\u00bb0px||0px||true|false\u00bb global_colors_info=\u00bb{}\u00bb][et_pb_row _builder_version=\u00bb4.27.4&#8243; width=\u00bb100%\u00bb max_width=\u00bb100%\u00bb custom_padding=\u00bb0px||0px||true|false\u00bb custom_margin=\u00bb0px||0px||true|false\u00bb global_colors_info=\u00bb{}\u00bb][et_pb_column type=\u00bb4_4&#8243; _builder_version=\u00bb4.27.4&#8243; 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details{background:#fff;border:1px solid var(--line);border-radius:14px;padding:2px 22px;margin:12px 0;}\n.mlk summary{cursor:pointer;font-weight:750;font-size:16.5px;padding:18px 0;list-style:none;display:flex;justify-content:space-between;align-items:center;}\n.mlk summary::-webkit-details-marker{display:none;}\n.mlk summary:after{content:'+';color:var(--red);font-size:24px;font-weight:600;}\n.mlk details[open] summary:after{content:'\\2013';}\n.mlk details p{color:var(--muted);line-height:1.65;margin:0 0 18px;font-size:15px;}\n.mlk .src{display:flex;flex-wrap:wrap;gap:10px;justify-content:center;margin-top:34px;}\n.mlk .chip{border:1px solid var(--line);background:#fff;border-radius:100px;padding:9px 18px;font-weight:750;font-size:14px;color:var(--ink);box-shadow:0 2px 8px rgba(0,0,0,.03);}\n.mlk .final{background:linear-gradient(155deg,#201e1c,#2d2723);color:#fff;text-align:center;}\n.mlk .final h2{color:#fff;}\n@media(prefers-reduced-motion:reduce){.mlk *{animation:none!important;}.mlk .rise{opacity:1;transform:none;}.mlk .draw,.mlk .drawp{stroke-dashoffset:0;}.mlk .flag{opacity:1;}}\n\/*ttkfix*\/.mlk .hero h1{color:#fff!important}.mlk h1 .hl{color:var(--accent)!important}.mlk h2{color:#232120!important}.mlk .final h2{color:#fff!important}.mlk h3{color:#232120!important}.mlk h4{color:#232120!important}.mlk .klab,.mlk .cch{color:#6f6b68!important}.mlk .atime{color:#6f6b68!important}<\/style>\n<div class=\"mlk\">\n<div class=\"hero\">\n<div class=\"w heroGrid\">\n<div>\n  <span class=\"eyebrow rise\">Machine Learning \u00b7 Predictivo<\/span><\/p>\n<h1 class=\"rise d1\">Vea la p\u00e9rdida <span class=\"hl\">antes de que llegue.<\/span><\/h1>\n<pee class=\"lead rise d2\">La capa de Machine Learning de TeepTrak aprende c\u00f3mo es lo \u00abnormal\u00bb en cada l\u00ednea y luego se\u00f1ala desviaciones, microparadas incipientes y problemas de calidad emergentes antes de que le cuesten una sola pieza \u2014 con la causa probable ya incluida.<\/pee>\n<ul class=\"ul rise d3\">\n<li><svg width=\"20\" height=\"20\" viewBox=\"0 0 20 20\"><circle cx=\"10\" cy=\"10\" r=\"9\" fill=\"#FF674C\"\/><path d=\"M6 10l2.5 2.5L14 7\" stroke=\"#201e1c\" stroke-width=\"2.2\" fill=\"none\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg>Funciona con los datos de TeepTrak que ya recopila<\/li>\n<li><svg width=\"20\" height=\"20\" viewBox=\"0 0 20 20\"><circle cx=\"10\" cy=\"10\" r=\"9\" fill=\"#FF674C\"\/><path d=\"M6 10l2.5 2.5L14 7\" stroke=\"#201e1c\" stroke-width=\"2.2\" fill=\"none\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg>Aviso anticipado \u2014 de minutos a horas antes del rebasamiento<\/li>\n<li><svg width=\"20\" height=\"20\" viewBox=\"0 0 20 20\"><circle cx=\"10\" cy=\"10\" r=\"9\" fill=\"#FF674C\"\/><path d=\"M6 10l2.5 2.5L14 7\" stroke=\"#201e1c\" stroke-width=\"2.2\" fill=\"none\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><\/svg>Explicable \u2014 cada aviso muestra el porqu\u00e9, no solo el qu\u00e9<\/li>\n<\/ul>\n<div class=\"cta rise d4\"><a class=\"btn btn-red\" href=\"https:\/\/teeptrak.com\/en\/demonstration\/\">Solicitar una demo<\/a><a class=\"btn btn-ghost\" href=\"#how\">Ver c\u00f3mo funciona<\/a><\/div>\n<\/p><\/div>\n<div class=\"rise d2\">\n<div class=\"dash\">\n<div class=\"dbar\"><img decoding=\"async\" class=\"dlogo\" src=\"https:\/\/teeptrak.com\/wp-content\/uploads\/2023\/05\/TeepTrak_All-Logos_TeepTrak_Logo-.svg\" alt=\"TeepTrak\"\/><span class=\"dctx\">Detecci\u00f3n de anomal\u00edas \u2014 Horno B2<\/span><span class=\"dlive\"><i><\/i>ALERTA<\/span><\/div>\n<div class=\"dbody\">\n<div class=\"card\">\n<div class=\"cch\"><span>Presi\u00f3n \u00b7 real vs. normal aprendida<\/span><span style=\"color:var(--red)\"><svg class=\"tt-ico\" viewBox=\"0 0 24 24\" width=\"1em\" height=\"1em\" fill=\"none\" stroke=\"currentColor\" stroke-width=\"2\" stroke-linecap=\"round\" stroke-linejoin=\"round\" style=\"display:inline-block;vertical-align:-0.125em\" aria-hidden=\"true\"><path d=\"M12 3 2 20h20z\"\/><line x1=\"12\" y1=\"10\" x2=\"12\" y2=\"14\"\/><line x1=\"12\" y1=\"17\" x2=\"12.01\" y2=\"17\"\/><\/svg> rebasamiento en ~25 min<\/span><\/div>\n<p>     <svg width=\"100%\" height=\"150\" viewBox=\"0 0 330 150\" preserveAspectRatio=\"none\">\n      <rect x=\"0\" y=\"60\" width=\"330\" height=\"40\" fill=\"#eafaf0\"\/>\n      <text x=\"6\" y=\"74\" fill=\"#16A34A\" font-size=\"9\" font-family=\"Arial\" font-weight=\"700\">normal aprendida<\/text>\n      <line x1=\"0\" y1=\"32\" x2=\"330\" y2=\"32\" stroke=\"#EB352C\" stroke-width=\"1.4\" stroke-dasharray=\"5 4\"\/>\n      <text x=\"324\" y=\"28\" fill=\"#EB352C\" font-size=\"9\" font-family=\"Arial\" font-weight=\"800\" text-anchor=\"end\">l\u00edmite superior<\/text>\n      <polyline class=\"draw\" points=\"6,92 46,88 86,90 126,82 166,84 206,74 240,66\" fill=\"none\" stroke=\"#232120\" stroke-width=\"2.6\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/>\n      <polyline class=\"drawp\" points=\"240,66 276,54 312,38\" fill=\"none\" stroke=\"#FF674C\" stroke-width=\"2.6\" stroke-dasharray=\"5 5\" stroke-linecap=\"round\"\/>\n      <g class=\"flag\"><circle class=\"flagdot\" cx=\"240\" cy=\"66\" r=\"6\" fill=\"#EB352C\"\/><circle cx=\"240\" cy=\"66\" r=\"11\" fill=\"none\" stroke=\"#EB352C\" stroke-width=\"1.5\" opacity=\".5\"\/><\/g>\n     <\/svg>\n    <\/div>\n<div class=\"kpis\">\n<div class=\"kpi kl\">\n<div class=\"klab\">Anomal\u00edas \u00b7 7 d<\/div>\n<div class=\"kval\">14<\/div>\n<div class=\"kd up\">detectadas a tiempo<\/div>\n<\/div>\n<div class=\"kpi\">\n<div class=\"klab\">Antelaci\u00f3n media<\/div>\n<div class=\"kval\">23 min<\/div>\n<div class=\"kd up\">antes del rebasamiento<\/div>\n<\/div>\n<div class=\"kpi\">\n<div class=\"klab\">Confianza<\/div>\n<div class=\"kval\">94%<\/div>\n<div class=\"kd up\">\u25b2 ajustada<\/div>\n<\/div>\n<div class=\"kpi\">\n<div class=\"klab\">Ruido de alertas<\/div>\n<div class=\"kval\">\u221280%<\/div>\n<div class=\"kd up\">vs. umbrales<\/div>\n<\/div><\/div>\n<div class=\"card\">\n<div class=\"cch\"><span>Avisos recientes<\/span><span>causa probable<\/span><\/div>\n<div class=\"arow\"><span class=\"adot\" style=\"background:#EB352C\"><\/span><span class=\"aname\">Deriva de presi\u00f3n<\/span><span class=\"asub\">\u00b7 Oven B2 \u00b7 valve wear<\/span><span class=\"atime\">11:20<\/span><\/div>\n<div class=\"arow\"><span class=\"adot\" style=\"background:#F59E0B\"><\/span><span class=\"aname\">Deriva de ciclo<\/span><span class=\"asub\">\u00b7 Line 4 \u00b7 tooling<\/span><span class=\"atime\">10:48<\/span><\/div>\n<div class=\"arow\"><span class=\"adot\" style=\"background:#16A34A\"><\/span><span class=\"aname\">Resuelto<\/span><span class=\"asub\">\u00b7 Temp \u00b7 Dryer 2<\/span><span class=\"atime\">09:55<\/span><\/div>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n<\/p><\/div>\n<\/div>\n<\/div>\n<div class=\"trust\">\n<div class=\"w\"><b>450+ factories<\/b> \u00b7 30+ countries \u00b7 <b>4.7\/5<\/b> on G2 &amp; Capterra<\/div>\n<\/div>\n<section>\n<div class=\"w\">\n<div class=\"kick rise\">Por qu\u00e9 Machine Learning<\/div>\n<h2 class=\"rise d1\">La se\u00f1al ya est\u00e1 en sus datos.<\/h2>\n<pee class=\"sub rise d2\">Recopila miles de puntos de datos por turno. El patr\u00f3n que predice el rechazo de esta noche est\u00e1 ah\u00ed \u2014 Machine Learning lo encuentra antes de que usted lo note.<\/pee>\n<div class=\"why\">\n<div class=\"wc rise d1\">\n<div class=\"ic\"><svg width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M3 17c3-9 6-9 9 0s6 9 9 0\" stroke=\"#fff\" stroke-width=\"2.2\" stroke-linecap=\"round\"\/><\/svg><\/div>\n<h3>Aprende su normalidad<\/h3>\n<pee>Una referencia viva por l\u00ednea y producto, construida a partir de tiempos de ciclo, paradas, par\u00e1metros y calidad. Ninguna l\u00ednea se juzga con la misma regla gen\u00e9rica.<\/pee><\/div>\n<div class=\"wc rise d2\">\n<div class=\"ic\"><svg width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" fill=\"none\"><path d=\"M3 14l5-5 4 3 8-9\" stroke=\"#fff\" stroke-width=\"2.2\" stroke-linecap=\"round\" stroke-linejoin=\"round\"\/><circle cx=\"20\" cy=\"4\" r=\"2.4\" fill=\"#fff\"\/><\/svg><\/div>\n<h3>Predice lo an\u00f3malo<\/h3>\n<pee>Los modelos de tendencia y patr\u00f3n avisan cuando a\u00fan hay tiempo de corregir \u2014 de minutos a horas de antelaci\u00f3n, no una autopsia el mes siguiente.<\/pee><\/div>\n<div class=\"wc rise d3\">\n<div class=\"ic\"><svg width=\"24\" height=\"24\" viewBox=\"0 0 24 24\" fill=\"none\"><circle cx=\"11\" cy=\"11\" r=\"7\" stroke=\"#fff\" stroke-width=\"2.2\"\/><path d=\"M16 16l5 5\" stroke=\"#fff\" stroke-width=\"2.2\" stroke-linecap=\"round\"\/><\/svg><\/div>\n<h3>Se explica solo<\/h3>\n<pee>Cada alerta llega con los factores que contribuyen y d\u00f3nde mirar primero. Una informaci\u00f3n en la que la planta conf\u00eda, no una caja negra que da falsas alarmas.<\/pee><\/div>\n<\/p><\/div>\n<\/div>\n<\/section>\n<section class=\"screens\">\n<div class=\"w\">\n<div class=\"kick rise\">Dentro del modelo<\/div>\n<h2 class=\"rise d1\">De se\u00f1ales en bruto a un aviso sobre el que actuar.<\/h2>\n<pee class=\"sub rise d2\">Cuatro formas en que el modelo convierte sus datos en previsi\u00f3n.<\/pee>\n<div class=\"sgrid\">\n<div class=\"scard rise d1\">\n<div class=\"shdr\"><span class=\"sdot\"><\/span><span class=\"lab\">Detecci\u00f3n de deriva<\/span><\/div>\n<div class=\"sbody\"><svg width=\"100%\" height=\"70\" viewBox=\"0 0 300 70\"><rect x=\"0\" y=\"28\" width=\"300\" height=\"20\" fill=\"#eafaf0\"\/><line x1=\"0\" y1=\"14\" x2=\"300\" y2=\"14\" stroke=\"#EB352C\" stroke-width=\"1.3\" stroke-dasharray=\"4 4\"\/><polyline class=\"draw\" points=\"4,44 60,42 116,38 172,30 228,22 286,14\" fill=\"none\" stroke=\"#232120\" stroke-width=\"2.4\" stroke-linecap=\"round\"\/><circle class=\"flagdot\" cx=\"228\" cy=\"22\" r=\"5\" fill=\"#EB352C\"\/><\/svg><\/div>\n<h4>Capta el deslizamiento lento<\/h4>\n<pee>La suave deriva del viernes por la tarde que ning\u00fan umbral detecta \u2014 se\u00f1alada en cuanto la tendencia cambia.<\/pee><\/div>\n<div class=\"scard rise d2\">\n<div class=\"shdr\"><span class=\"sdot\"><\/span><span class=\"lab\">Agrupaci\u00f3n de patrones<\/span><\/div>\n<div class=\"sbody\"><svg width=\"100%\" height=\"70\" viewBox=\"0 0 300 70\"><g fill=\"#cfccc9\"><circle cx=\"40\" cy=\"24\" r=\"4\"\/><circle cx=\"55\" cy=\"34\" r=\"4\"\/><circle cx=\"48\" cy=\"46\" r=\"4\"\/><circle cx=\"66\" cy=\"40\" r=\"4\"\/><\/g><g fill=\"#FF674C\"><circle cx=\"150\" cy=\"20\" r=\"4\"\/><circle cx=\"166\" cy=\"30\" r=\"4\"\/><circle cx=\"158\" cy=\"42\" r=\"4\"\/><\/g><g fill=\"#EB352C\"><circle cx=\"250\" cy=\"38\" r=\"5\"\/><circle cx=\"264\" cy=\"48\" r=\"5\"\/><circle cx=\"240\" cy=\"50\" r=\"5\"\/><\/g><ellipse cx=\"252\" cy=\"46\" rx=\"26\" ry=\"18\" fill=\"none\" stroke=\"#EB352C\" stroke-width=\"1.4\" stroke-dasharray=\"3 3\"\/><\/svg><\/div>\n<h4>Encuentra el fallo recurrente<\/h4>\n<pee>Agrupa eventos similares para que aflore una causa ra\u00edz recurrente \u2014 incluso entre turnos y productos.<\/pee><\/div>\n<div class=\"scard rise d3\">\n<div class=\"shdr\"><span class=\"sdot\"><\/span><span class=\"lab\">Alerta explicable<\/span><\/div>\n<div class=\"sbody\"><svg width=\"100%\" height=\"70\" viewBox=\"0 0 300 70\" font-family=\"Arial\"><g><rect x=\"60\" y=\"8\" width=\"180\" height=\"12\" rx=\"3\" fill=\"#efeeed\"\/><rect class=\"lfill\" x=\"60\" y=\"8\" width=\"180\" height=\"12\" rx=\"3\" fill=\"#EB352C\" style=\"--p:.9\"\/><text x=\"0\" y=\"18\" fill=\"#232120\" font-size=\"10\" font-weight=\"700\">valve<\/text><rect x=\"60\" y=\"28\" width=\"180\" height=\"12\" rx=\"3\" fill=\"#efeeed\"\/><rect class=\"lfill\" x=\"60\" y=\"28\" width=\"180\" height=\"12\" rx=\"3\" fill=\"#FF674C\" style=\"--p:.6\"\/><text x=\"0\" y=\"38\" fill=\"#232120\" font-size=\"10\" font-weight=\"700\">ambient<\/text><rect x=\"60\" y=\"48\" width=\"180\" height=\"12\" rx=\"3\" fill=\"#efeeed\"\/><rect class=\"lfill\" x=\"60\" y=\"48\" width=\"180\" height=\"12\" rx=\"3\" fill=\"#cfccc9\" style=\"--p:.3\"\/><text x=\"0\" y=\"58\" fill=\"#232120\" font-size=\"10\" font-weight=\"700\">recipe<\/text><\/g><\/svg><\/div>\n<h4>Muestra el porqu\u00e9<\/h4>\n<pee>Cada aviso prioriza los factores que lo provocan, para que el equipo empiece por la causa m\u00e1s probable \u2014 no desde cero.<\/pee><\/div>\n<div class=\"scard rise d4\">\n<div class=\"shdr\"><span class=\"sdot\"><\/span><span class=\"lab\">Cronolog\u00eda de anomal\u00edas<\/span><\/div>\n<div class=\"sbody\"><svg width=\"100%\" height=\"70\" viewBox=\"0 0 300 70\"><line x1=\"0\" y1=\"50\" x2=\"300\" y2=\"50\" stroke=\"#e7e6e5\" stroke-width=\"2\"\/><g><circle cx=\"50\" cy=\"50\" r=\"5\" fill=\"#16A34A\"\/><circle cx=\"120\" cy=\"50\" r=\"5\" fill=\"#F59E0B\"\/><circle cx=\"190\" cy=\"50\" r=\"5\" fill=\"#16A34A\"\/><circle class=\"flagdot\" cx=\"262\" cy=\"50\" r=\"6\" fill=\"#EB352C\"\/><\/g><rect class=\"bar\" x=\"256\" y=\"14\" width=\"12\" height=\"26\" rx=\"3\" fill=\"#EB352C\" style=\"animation-delay:.5s;transform-origin:bottom\"\/><\/svg><\/div>\n<h4>Un registro claro<\/h4>\n<pee>Cada anomal\u00eda, registrada con su contexto \u2014 para sus rutinas de mejora continua, su BI o su sistema de mantenimiento.<\/pee><\/div>\n<\/p><\/div>\n<\/div>\n<\/section>\n<section id=\"how\">\n<div class=\"w\">\n<div class=\"kick rise\">How it works<\/div>\n<h2 class=\"rise d1\">Sin proyecto de datos. Funciona con lo que ya tiene.<\/h2>\n<div class=\"steps\">\n<div class=\"step rise d1\">\n<div class=\"n\">1<\/div>\n<h3>Alim\u00e9ntelo con sus datos<\/h3>\n<pee>OEE, paradas, tiempos de ciclo, par\u00e1metros de proceso y calidad \u2014 ya fluyen por TeepTrak. Nada nuevo que instalar, sin sensores adicionales.<\/pee><\/div>\n<div class=\"step rise d2\">\n<div class=\"n\">2<\/div>\n<h3>Aprende lo normal<\/h3>\n<pee>El modelo construye una referencia para cada l\u00ednea y producto y la refina a medida que cambian las condiciones. Su planta, no un manual.<\/pee><\/div>\n<div class=\"step rise d3\">\n<div class=\"n\">3<\/div>\n<h3>Avisa pronto<\/h3>\n<pee>Las predicciones llegan donde su equipo ya trabaja \u2014 panel, correo, smartphone o API \u2014 cada una con la causa probable.<\/pee><\/div>\n<\/p><\/div>\n<\/div>\n<\/section>\n<section class=\"stats\">\n<div class=\"w\">\n<div class=\"srow\">\n<div class=\"rise d1\">\n<div class=\"v\">23 min<\/div>\n<div class=\"l\">de aviso medio antes del rebasamiento<\/div>\n<\/div>\n<div class=\"rise d2\">\n<div class=\"v\">\u221280%<\/div>\n<div class=\"l\">de ruido de alertas vs. umbrales fijos<\/div>\n<\/div>\n<div class=\"rise d3\">\n<div class=\"v\">0<\/div>\n<div class=\"l\">sensores nuevos necesarios<\/div>\n<\/div>\n<div class=\"rise d4\">\n<div class=\"v\">94%<\/div>\n<div class=\"l\">de confianza, y subiendo<\/div>\n<\/div>\n<\/div>\n<\/div>\n<\/section>\n<section>\n<div class=\"w\">\n<div class=\"kick rise\">Funciona sobre su stack<\/div>\n<h2 class=\"rise d1\">Construido sobre los datos que TeepTrak ya recopila.<\/h2>\n<div class=\"src rise d2\"><span class=\"chip\">PerfTrak<\/span><span class=\"chip\">QualTrak<\/span><span class=\"chip\">ProcessTrak<\/span><span class=\"chip\">OPC UA<\/span><span class=\"chip\">MES<\/span><span class=\"chip\">BI \/ data lake<\/span><span class=\"chip\">REST API<\/span><\/div>\n<div class=\"faq\">\n<details class=\"rise\">\n<summary>\u00bfCu\u00e1ntos datos necesita?<\/summary>\n<pee>Si ya usa TeepTrak, tiene suficientes. El modelo se entrena con el historial recopilado y empieza a revelar patrones en d\u00edas \u2014 y mejora cuanto m\u00e1s ve.<\/pee><\/details>\n<details class=\"rise\">\n<summary>\u00bfEs una caja negra?<\/summary>\n<pee>No \u2014 ese es el punto. Cada alerta prioriza los factores que la provocaron e indica d\u00f3nde mirar primero, para que su equipo act\u00fae sobre informaci\u00f3n verificable, no sobre una puntuaci\u00f3n misteriosa.<\/pee><\/details>\n<details class=\"rise\">\n<summary>\u00bfD\u00f3nde aparecen las predicciones?<\/summary>\n<pee>Donde su equipo ya trabaja: paneles de TeepTrak, alertas en smartphone, correo, o enviadas a su BI, MES o sistema de mantenimiento v\u00eda API.<\/pee><\/details>\n<details class=\"rise\">\n<summary>\u00bfNecesitamos un cient\u00edfico de datos?<\/summary>\n<pee>No. TeepTrak configura y ajusta los modelos con usted. Usted recibe los avisos anticipados y las explicaciones; del machine learning nos encargamos nosotros.<\/pee><\/details>\n<\/p><\/div>\n<\/div>\n<\/section>\n<section class=\"final\">\n<div class=\"w\">\n<h2 class=\"rise\">Act\u00fae antes de que ocurra la p\u00e9rdida.<\/h2>\n<pee class=\"sub rise d1\" style=\"color:#cfccc9\">Una sesi\u00f3n de trabajo de 30 minutos con un ingeniero de TeepTrak \u2014 revisamos sus propios datos y mostramos d\u00f3nde el modelo le habr\u00eda avisado primero.<\/pee>\n<div class=\"cta rise d2\" style=\"justify-content:center;margin-top:28px\"><a class=\"btn btn-red\" href=\"https:\/\/teeptrak.com\/en\/demonstration\/\">Solicitar mi demo<\/a><a class=\"btn btn-ghost\" href=\"https:\/\/teeptrak.com\/en\/solutions\/\">Todas las soluciones<\/a><\/div>\n<\/div>\n<\/section>\n<\/div>\n<p>[\/et_pb_code][\/et_pb_column][\/et_pb_row][\/et_pb_section]<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Saca el m\u00e1ximo partido a tus datos de producci\u00f3n con TeepTrak AI. Anticipa las aver\u00edas, mejora el rendimiento y optimiza tu OEE con el aprendizaje autom\u00e1tico. <\/p>\n","protected":false},"author":7,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","footnotes":""},"class_list":["post-32714","page","type-page","status-publish","hentry"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.8 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Machine Learning - TEEPTRAK<\/title>\n<meta name=\"description\" content=\"Machine Learning detecci\u00f3n de anomal\u00edas: TEEPTRAK usa algoritmos IA para optimizar tus procesos industriales y mejorar tu OEE.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/teeptrak.com\/es\/machine-learning-deteccion-anomalias\/\" \/>\n<meta property=\"og:locale\" content=\"es_ES\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Machine Learning - 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