The Challenge
Maintenance was either reactive or on a fixed calendar, so parts with life left were replaced while real failures still happened.
The Solution
Azure IoT Hub ingesting sensor telemetry, stream processing for anomaly detection, and Power BI dashboards on the shop floor.
📋 Overview
An industrial group needed to move from reactive maintenance — fix it when it breaks — and calendar-based preventive maintenance — replace it every X months — to a predictive model based on the actual condition of each machine.
🎯 The Challenge
Business context Unplanned stoppages on a continuous production line cost thousands of euros an hour. Preventive maintenance avoided some failures but frequently meant replacing parts that still had useful life, wasting resources.
Technical requirements
- Real-time ingestion: read sensor data — temperature, vibration, cycles — every second.
- Anomalies: detect the subtle wear patterns a person cannot see.
- Visualisation: clear dashboards on the shop floor, for operators.
- Alerting: notify maintenance before a critical failure.
💡 The Solution
Strategic approach An Azure IoT Hub architecture for collection, stream processing for real-time analysis, and Power BI for historical and trend visualisation.
Technical implementation
- Connectivity: edge devices collect data from CNC machines over OPC-UA.
- Processing: Azure Stream Analytics handles telemetry in flight.
- Machine learning: a model trained to find correlations between vibration, temperature and historical failures.
- Power BI real-time streaming: push datasets update the health status of each machine automatically.
Timeline
- Phase 1 (IoT connectivity): 4 weeks
- Phase 2 (data science and modelling): 6 weeks
- Phase 3 (dashboard development): 3 weeks
- Phase 4 (deployment and calibration): 4 weeks
📈 The Results
Success metrics
- 🛑 Downtime: 30% fewer hours lost to unexpected breakdowns.
- 🔧 Parts: component service life extended 15%, by replacing at the right moment.
- 📊 Decisions: production planning aligned to the real condition of the machines.
Business impact The plant became more reliable and more predictable. Maintenance stopped firefighting and started planning precise interventions in windows that do not affect customer delivery.
🛠️ Technology Stack
- Azure IoT Hub: secure entry point for sensor data.
- Azure Synapse: big data warehouse.
- Power BI: operational and strategic visualisation.
💬 Client Testimonial
“We can see the pulse of the plant in real time. Knowing a machine will fail three days before it does completely changes our game.”
Maintenance Director Industrial Group
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