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Per als models de manteniment predictiu, utilitzem tant dades hist\u00f2riques com dades en temps real per anticipar fallades dels equips o necessitats de manteniment<\/em>. En analitzar les dades dels sensors, els registres de manteniment i altra informaci\u00f3 rellevant, podem programar el manteniment de manera proactiva, reduir el temps d\u2019inactivitat i prolongar la vida \u00fatil de la maquin\u00e0ria.<\/p>\n Les dades de diferents vehicles, eines i dispositius de producci\u00f3 es poden utilitzar per predir quan un sistema necessita manteniment, per despr\u00e9s programar el manteniment preventiu i les operacions de control de qualitat<\/strong>.<\/p>\n Aquesta predicci\u00f3 de les fallades de les peces de maquin\u00e0ria \u00e9s molt precisa, ja que utilitza les dades dels sensors d\u2019IoT, reduint el temps d\u2019inactivitat dels equips, les fallades de les eines i les necessitats de manteniment. En monitorar l\u2019estat dels equips i la infraestructura en temps real (poden ser dades com el seguiment de vibracions, l\u2019\u00fas recurrent de m\u00e0quines en un ordre espec\u00edfic, temperatures i pressions, etc.) podem comprendre l\u2019estat de funcionament t\u00edpic de l\u2019equip i rastrejar-ne les variacions.<\/p>\n Mitjan\u00e7ant l\u2019an\u00e0lisi de les dades hist\u00f2riques de manteniment, podem generar models descriptius per comprendre les causes recurrents del manteniment i despr\u00e9s poder predir quan i on ser\u00e0 necessari el manteniment en el futur.<\/p>\n El nostre enfocament \u00e9s proactiu i allarga la vida \u00fatil dels actius<\/strong> assegurant un funcionament fluid, cosa que resulta en una reducci\u00f3 de costos<\/strong>, reduint tamb\u00e9 aix\u00ed l\u2019impacte ambiental<\/strong> i millorant la competitivitat de l\u2019empresa<\/strong>.<\/p>\n [\/et_pb_text][et_pb_image src=\u00bbhttps:\/\/www.mosaicfactor.com\/wp-content\/uploads\/2024\/12\/mantenimiento-predictivo-es.svg\u00bb alt=\u00bbpredictive-maintenance-mosaicfactor\u00bb title_text=\u00bbmantenimiento-predictivo-es\u00bb _builder_version=\u00bb4.27.2″ _module_preset=\u00bbdefault\u00bb max_width=\u00bb300px\u00bb module_alignment=\u00bbcenter\u00bb global_colors_info=\u00bb{}\u00bb theme_builder_area=\u00bbpost_content\u00bb][\/et_pb_image][et_pb_text _builder_version=\u00bb4.27.2″ _module_preset=\u00bbdefault\u00bb text_font_size=\u00bb18px\u00bb text_line_height=\u00bb1.6em\u00bb custom_margin=\u00bb||1em||false|false\u00bb global_colors_info=\u00bb{}\u00bb theme_builder_area=\u00bbpost_content\u00bb]<\/p>\n Utilitzem principalment el manteniment predictiu en els sectors seg\u00fcents:<\/p>\n [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=\u00bb4.27.2″ _module_preset=\u00bbdefault\u00bb width=\u00bb100%\u00bb max_width=\u00bb900px\u00bb 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En analitzar les dades dels sensors, els registres de manteniment i altra informaci\u00f3 rellevant, podem programar el manteniment de manera proactiva, reduir el temps d\u2019inactivitat i prolongar la vida \u00fatil de la maquin\u00e0ria.<\/p>\n","protected":false},"author":1,"featured_media":5290,"parent":5289,"menu_order":0,"template":"","format":"standard","meta":{"_acf_changed":false,"_et_pb_use_builder":"on","_et_pb_old_content":"","_et_gb_content_width":"","inline_featured_image":false,"footnotes":""},"industry":[],"proyecto":[],"class_list":["post-5296","solution","type-solution","status-publish","format-standard","has-post-thumbnail","hentry"],"acf":[],"yoast_head":"\n\n
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