{"id":18749,"date":"2025-10-16T08:30:00","date_gmt":"2025-10-16T06:30:00","guid":{"rendered":"https:\/\/spo-comm.de\/predictive-maintenance-when-machines-look-ahead\/"},"modified":"2026-04-13T12:17:42","modified_gmt":"2026-04-13T10:17:42","slug":"predictive-maintenance-when-machines-look-ahead","status":"publish","type":"post","link":"https:\/\/spo-comm.de\/en\/predictive-maintenance-when-machines-look-ahead\/","title":{"rendered":"Predictive maintenance – when machines look ahead"},"content":{"rendered":"\n

What is behind predictive maintenance?<\/strong><\/h2>\n\n

While traditional maintenance involves checking systems at fixed intervals, predictive maintenance is one step ahead. Data from the machine itself, such as temperature, vibration or power consumption, is continuously recorded and analyzed. With the help of algorithms and AI models, conclusions can be drawn as to when a component is likely to fail. This means that maintenance can be carried out exactly when it is really necessary, i.e. not too early and not too late. <\/p>\n\n

Predictive maintenance describes a data-based maintenance approach in which sensor values are analyzed and failure probabilities are calculated on this basis in order to avoid unplanned downtimes.<\/p>\n\n

How does this work in practice?<\/h2>\n\n

The basic principle is simple: machines are equipped with sensors that continuously supply data. This data is collected, processed and analyzed either directly in the machine or via an edge system. <\/p>\n\n

The procedure is usually as follows:<\/p>\n\n

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  1. Data acquisition<\/span>: Sensors measure vibrations, temperatures, voltages, etc.<\/li>\n\n\n\n
  2. Data transmission<\/span>: The information is passed on via secure networks or IoT gateways.<\/li>\n\n\n\n
  3. Analysis<\/span>: AI models or machine learning<\/a> algorithms recognize patterns, anomalies or trends.<\/li>\n\n\n\n
  4. Forecast<\/span>: If the system detects deviations from the normal state, a maintenance requirement is reported in good time before a failure occurs.<\/li>\n<\/ol>\n\n

    For these processes to function smoothly, reliable hardware is needed at the edge. After all, not all data can or should always be sent to the cloud – low latency times, data protection and real-time requirements often make local processing necessary. <\/p>\n\n

    This is where mini PCs like our CORE 5 Ultra<\/a> come into play: thanks to the Intel\u00ae Core\u2122 Ultra 5 125U processor (with 2 Performance Cores & 10 Efficiency Cores) and integrated NPU <\/a>with 11 TOPS, it can run AI models directly on the spot. This allows sensor data to be analyzed in real time and potential faults to be detected before they occur. <\/p>\n\n

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    Discover the CORE 5 Ultra!<\/a><\/div>\n<\/div>\n\n

    Advantages for industry and users<\/h2>\n\n

    The advantages of predictive maintenance are obvious:<\/p>\n\n