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This invention describes a system that learns how a factory should perform based on its past sensor data and process steps. It then constantly monitors current factory data to spot any deviations from normal operation, identifying which specific sensors or process steps are causing the problem. The system then automatically triggers a maintenance action, often by looking up the fault in a database, to fix the issue.
Why it matters: Filed before advanced machine learning and industrial IoT became widespread. The computational power and data infrastructure needed for complex tensor models and real-time factory control are now more accessible.
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