{"id":1248,"date":"2026-05-01T16:05:42","date_gmt":"2026-05-01T14:05:42","guid":{"rendered":"https:\/\/dejonge.ai\/use-cases\/predictive-maintenance\/"},"modified":"2026-05-01T17:03:50","modified_gmt":"2026-05-01T15:03:50","slug":"predictive-maintenance","status":"publish","type":"page","link":"https:\/\/dejonge.ai\/en\/use-cases\/predictive-maintenance\/","title":{"rendered":"Predictive Maintenance"},"content":{"rendered":"<section class=\"l-section wpb_row us_custom_5003792f height_medium with_shape\"><div class=\"l-section-shape type_custom pos_bottom\" style=\"height:15vmin;\"><svg xmlns=\"http:\/\/www.w3.org\/2000\/svg\" viewBox=\"0 0 64 8\" preserveAspectRatio=\"none\" width=\"100%\" height=\"100%\">\r\n\t<path fill=\"currentColor\" d=\"M64 8 L0 8 L0 0 L64 0 Z\"\/>\r\n<\/svg><\/div><div class=\"l-section-h i-cf\"><div class=\"g-cols vc_row via_flex valign_top type_default stacking_default\"><div class=\"vc_col-sm-12 wpb_column vc_column_container\"><div class=\"vc_column-inner\"><div class=\"wpb_wrapper\"><h1 class=\"w-post-elm post_title us_custom_b3ce43a6 has_text_color entry-title color_link_inherit\">Predictive Maintenance<\/h1><div class=\"w-separator size_custom\" style=\"height:10vh\"><\/div><\/div><\/div><\/div><\/div><\/div><\/section><section class=\"l-section wpb_row height_medium\"><div class=\"l-section-h i-cf\"><div class=\"g-cols vc_row via_flex valign_top type_default stacking_default\"><div class=\"vc_col-sm-3 wpb_column vc_column_container\"><div class=\"vc_column-inner\"><div class=\"wpb_wrapper\"><div class=\"w-btn-wrapper align_none\"><a class=\"w-btn us-btn-style_7 icon_atleft\" href=\"https:\/\/dejonge.ai\/en\/use-cases\/\"><i class=\"fas fa-angle-left\"><\/i><span class=\"w-btn-label\">Use cases<\/span><\/a><\/div><\/div><\/div><\/div><div class=\"vc_col-sm-6 wpb_column vc_column_container\"><div class=\"vc_column-inner\"><div class=\"wpb_wrapper\"><h2 class=\"w-text us_custom_eb849559\"><span class=\"w-text-h\"><span class=\"w-text-value\">Maintenance when it is really needed.<\/span><\/span><\/h2><div class=\"w-post-elm post_image us_custom_60e9caca us_animate_this stretched\"><img loading=\"lazy\" decoding=\"async\" width=\"1024\" height=\"683\" src=\"https:\/\/dejonge.ai\/wp-content\/uploads\/2026\/05\/PM_engl-1024x683.png\" class=\"attachment-large size-large wp-post-image\" alt=\"\" srcset=\"https:\/\/dejonge.ai\/wp-content\/uploads\/2026\/05\/PM_engl-1024x683.png 1024w, https:\/\/dejonge.ai\/wp-content\/uploads\/2026\/05\/PM_engl-300x200.png 300w, https:\/\/dejonge.ai\/wp-content\/uploads\/2026\/05\/PM_engl-1280x853.png 1280w, https:\/\/dejonge.ai\/wp-content\/uploads\/2026\/05\/PM_engl-400x267.png 400w, https:\/\/dejonge.ai\/wp-content\/uploads\/2026\/05\/PM_engl-600x400.png 600w, https:\/\/dejonge.ai\/wp-content\/uploads\/2026\/05\/PM_engl.png 1536w\" sizes=\"auto, (max-width: 1024px) 100vw, 1024px\" \/><\/div><div class=\"w-separator size_small\"><\/div><div class=\"wpb_text_column\"><div class=\"wpb_wrapper\"><h3>The task<\/h3>\n<p>Maintenance according to a fixed calendar wastes downtime on healthy machines and risks damaging components that are already under strain. The gap between planned and actually required maintenance is the biggest hidden cost driver in most manufacturing companies. <\/p>\n<p>Predictive maintenance closes this gap. Not with gut instinct, but with signals that every machine sends out anyway. EdgeBrain learns to read these signals directly at the machine, without going through a data center.  <\/p>\n<h3>The challenge<\/h3>\n<p>Machine signals are diverse: vibration, temperature, current, pressure, sound emission. Each sensor type provides a different data structure and different information about the machine status. Conventional monitoring systems are often designed for a single sensor type and therefore only provide partial images. Only the combination shows whether a temperature increase is caused by increased load, wear or a changed machining program.   <\/p>\n<p>Added to this are the structural hurdles in practice. Interventions in PLC landscapes, IT\/OT architectures or ongoing processes are difficult to implement in most plants. Predictive maintenance systems, which require precisely this, fail due to the reality of existing plants.<\/p>\n<\/div><\/div><div class=\"w-separator size_medium\"><\/div><div class=\"wpb_text_column\"><div class=\"wpb_wrapper\"><h3>The solution<\/h3>\n<p>EdgeBrain is an open, sensor-independent system for predictive maintenance. The platform merges data from a wide range of industrial sensors to create a common status image of the machine. Vibration, temperature, current, pressure and noise emissions are processed inline on the device and interpreted together.  <\/p>\n<p>The basis is ML fingerprinting. EdgeBrain learns the normal condition of each machine in the first few operating cycles and recognizes any deviation from this from the first cut. Two complementary fingerprints enable seamless condition monitoring. Fingerprint 1 accompanies the tool life continuously. Its amplitude correlates directly with mechanical wear and turns a binary alarm into a real condition system. Fingerprint 2 shows a stable pattern for most of the tool&#8217;s life and only rises sharply shortly before the end of its life. This early warning is available in real time so that tool changes can be scheduled for the next planned stop.      <\/p>\n<p>EdgeBrain works completely autonomously. Processing, modeling and decision logic run locally on the device, without network dependency, without cloud, without latency. Sensors are attached using magnets and the device is connected as an inline adapter. Existing installations become data-driven systems in just a few minutes, without downtime and without interfering with existing systems.   <\/p>\n<h3>Key technical data<\/h3>\n<table style=\"width: 100%; border-collapse: collapse; margin: 1em 0;\">\n<thead>\n<tr style=\"background: #f5f5f5;\">\n<th style=\"padding: 8px; border: 1px solid #ddd; text-align: left;\">Parameter<\/th>\n<th style=\"padding: 8px; border: 1px solid #ddd; text-align: left;\">Value<\/th>\n<\/tr>\n<\/thead>\n<tbody>\n<tr>\n<td style=\"padding: 8px; border: 1px solid #ddd;\">Sensors<\/td>\n<td style=\"padding: 8px; border: 1px solid #ddd;\">Vibration, temperature, current, pressure, acoustic emission, other industrial sensors<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 8px; border: 1px solid #ddd;\">Data fusion<\/td>\n<td style=\"padding: 8px; border: 1px solid #ddd;\">Multimodal fusion of multiple sensor channels in real time<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 8px; border: 1px solid #ddd;\">Detection<\/td>\n<td style=\"padding: 8px; border: 1px solid #ddd;\">Continuous status tracking, early warning at the end of tool life<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 8px; border: 1px solid #ddd;\">Learning mode<\/td>\n<td style=\"padding: 8px; border: 1px solid #ddd;\">Unsupervised baseline recording in the first operating cycles<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 8px; border: 1px solid #ddd;\">Operating conditions<\/td>\n<td style=\"padding: 8px; border: 1px solid #ddd;\">-40 to +85 \u00b0C, passively cooled, 90 x 75 x 25.5 mm<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 8px; border: 1px solid #ddd;\">Infrastructure<\/td>\n<td style=\"padding: 8px; border: 1px solid #ddd;\">Completely self-sufficient, no intervention in PLC, MES, SCADA or cloud<\/td>\n<\/tr>\n<tr>\n<td style=\"padding: 8px; border: 1px solid #ddd;\">Integration<\/td>\n<td style=\"padding: 8px; border: 1px solid #ddd;\">Optional in MES, SCADA, ERP via encrypted connectivity<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n<h3>The added value<\/h3>\n<p>Predictive maintenance with EdgeBrain reduces unplanned downtimes by up to 85% and makes better use of tool life by up to 20%. Maintenance is decoupled from the calendar and is based on the actual condition of the machine. This reduces spare parts costs, increases system availability and allows maintenance teams to work in a planned rather than reactive manner.  <\/p>\n<p>The open sensor architecture allows the system to grow in line with demand. From a single critical component to an entire machine. Without new infrastructure, without risk to existing processes and with full data sovereignty in the plant.<\/p>\n<\/div><\/div><div class=\"w-separator size_small\"><\/div><div class=\"w-btn-wrapper align_none\"><a class=\"w-btn us-btn-style_1 icon_atright\" href=\"https:\/\/dejonge.ai\/en\/contact-us\/\"><span class=\"w-btn-label\">Contact us<\/span><i class=\"fas fa-angle-right\"><\/i><\/a><\/div><\/div><\/div><\/div><div class=\"vc_col-sm-3 wpb_column vc_column_container\"><div class=\"vc_column-inner\"><div class=\"wpb_wrapper\"><\/div><\/div><\/div><\/div><\/div><\/section>\n","protected":false},"excerpt":{"rendered":"Predictive MaintenanceUse casesMaintenance when it is really needed.The task Maintenance according to a fixed calendar wastes downtime on healthy machines and risks damaging components that are already under strain. The gap between planned and actually required maintenance is the biggest hidden cost driver in most manufacturing companies. Predictive maintenance closes this gap. Not with gut...","protected":false},"author":1,"featured_media":1247,"parent":333,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_acf_changed":false,"footnotes":""},"class_list":["post-1248","page","type-page","status-publish","has-post-thumbnail","hentry"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v27.5 - https:\/\/yoast.com\/product\/yoast-seo-wordpress\/ -->\n<title>Predictive Maintenance - de Jonge GmbH<\/title>\n<meta name=\"description\" content=\"Predictive maintenance with EdgeBrain: AI recognizes maintenance requirements directly at the machine, reduces downtime and makes existing systems more transparent without PLC intervention.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dejonge.ai\/en\/use-cases\/predictive-maintenance\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Predictive Maintenance - 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