Industrial Predictive Maintenance
Eliminating unplanned factory downtime with IoT telemetry and machine learning.
Project Overview
ManuCore operates 12 heavy industrial manufacturing plants across North America. Unplanned machinery breakdowns previously cost up to $150,000 per hour in idle production lines. Encelion implemented an edge-to-cloud predictive maintenance platform that detects mechanical anomalies weeks before failure occurs.
What the Client Faced
Vibration sensors and thermal telemetry on CNC mills, stamping presses, and robotic arms generated vast data streams that plant engineers had to inspect manually. Sudden bearing failures and motor burnouts led to expensive emergency repairs and missed delivery deadlines.
How We Solved It
Encelion deployed an end-to-end IoT data ingestion system coupled with time-series anomaly detection algorithms. Edge processors stream sensor readings (vibration, heat, acoustics, power draw) into an ML pipeline that forecasts Remaining Useful Life (RUL) for every critical component.
Edge Sensor Ingestion
Connected 1,200+ vibration and thermal sensors via MQTT protocol to local edge gateways.
Time-Series Anomaly Detection
Built LSTM and XGBoost models trained on 3 years of historical sensor logs and failure events.
Automated Work Orders
Integrated predictive alerts with SAP Plant Maintenance to auto-generate technician work orders.
Plant Manager Dashboard
Created real-time 3D twin visualization of factory floor status with health status indicators.
Explore Other Case Studies
Ready to Achieve Similar Results for Your Business?
Schedule a free 30-minute AI strategy consultation with our senior engineers.