Client Details
A global manufacturing conglomerate with operations in 30+ countries, producing industrial machinery and heavy equipment for sectors such as mining, construction, and energy. The company manages a fleet of over 200,000 connected machines and generates terabytes of telemetry data daily.
Challenge
The client faced unplanned equipment downtime costing millions annually. Existing predictive maintenance relied on batch analysis of sensor logs, which delayed failure detection and led to suboptimal scheduling of field technicians. Latency in analytics pipelines also meant early warning signals were often missed.
Additional challenges included:
- High-volume, high-velocity streaming data from 1M+ IoT sensors.
- Inconsistent ML deployment across edge devices with limited compute.
- Lack of centralized model monitoring and failure explainability.
Solution
The company embarked on a large-scale deployment of Edge AI using Azure Percept, Azure IoT Hub, and Databricks MLflow, enabling real-time predictive maintenance across all operational geographies.
Key Components
- Model Development
- Deep learning LSTM models were trained on historical vibration, pressure, and temperature data to predict Remaining Useful Life (RUL).
- A transformer-based anomaly detection model was developed to detect multivariate sensor anomalies in real time.
- Feature engineering pipelines were built with Delta Lake and scheduled using Databricks Workflows.
- Edge Deployment
- Models were packaged using ONNX and deployed to ruggedized edge gateways running Azure IoT Edge in remote field locations.
- Lightweight inference engines on the edge provided sub-second predictions, with fallback to cloud when connectivity dropped.
- Streaming & Orchestration
- Azure Stream Analytics and Kafka were used to ingest live sensor data, feeding it to both edge and cloud models.
- Event-driven alerts were published to Microsoft Teams and Azure Logic Apps to trigger technician dispatch and order spare parts.
- MLOps & Monitoring
- End-to-end model lifecycle management via MLflow, including auto-retraining triggers based on model drift and edge telemetry logs.
- Integrated dashboards with Azure Monitor and Power BI provided real-time operational health, prediction accuracy, and anomaly reports.
The Impact
✅ Downtime Reduction: Reduced unexpected machine failures by 42% in the first 6 months.
✅ Response Time: Cut anomaly-to-alert time from 15 minutes to under 5 seconds.
✅ Edge Resilience: Enabled AI predictions at the source—even in low-connectivity environments.
✅ Cost Savings: Saved an estimated $28M annually through early detection, optimized field operations, and extended machine life.
✅ Scalability: Architecture designed to support multi-model deployment across 5,000+ facilities globally.
