Thousands of IoT sensors on the production floor were generating real-time machine data that was being discarded. A real-time Azure pipeline captured, cleaned, and landed it in Microsoft Fabric for AI-powered predictive maintenance.
The plant had 3,200 IoT sensors generating temperature, vibration, pressure, and cycle count data every 30 seconds. There was no pipeline to capture it — the data was being overwritten in the sensor buffers and lost, making predictive maintenance impossible. Equipment failures were only discovered after they occurred, causing costly unplanned downtime.
Connected all 3,200 sensors to Azure IoT Hub, configuring device twins and message routing to stream telemetry data in real time to Azure Event Hubs for downstream processing.
Deployed Azure Stream Analytics jobs to filter out sensor noise, detect out-of-range readings, and apply real-time quality rules before data was passed to the storage layer.
Configured streaming pipelines to land clean, validated sensor readings into a Microsoft Fabric Lakehouse in Delta format — enabling both real-time queries and historical batch analysis.
Used Fabric's integrated Azure Machine Learning to train a failure prediction model on 18 months of historical sensor data and known failure events, achieving 87% prediction accuracy at 48-hour horizon.
Built a Power BI dashboard displaying real-time sensor health, AI failure risk scores per machine, and maintenance work order recommendations — integrated with the CMMS via Power Automate.
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