Industrial IoT Edge Monitoring & Digital Twin Platform
Connecting 4,500 industrial robotic arms to predict mechanical failures 72 hours before breakdown.
Key Business & Engineering Outcomes
Reduction in catastrophic machinery breakdowns
Prevented unbudgeted emergency repair and idle labor costs
Advance notice given to maintenance teams before part failure
Real-time telemetry stream processed across 8 factories
The Business & Technical Challenge
Factory floor machinery breakdowns were costing $45,000 per hour in idle production lines. Sensor data was siloed and analyzed retrospectively rather than predictively.
The Entecra Architectural Solution
We designed a ruggedized MQTT edge network with embedded TensorFlow Lite models streaming anomaly alerts to a central 3D digital twin dashboard.
Architecture Blueprint & Implementation Phases
Edge Sensor Gateway Mesh
Installed containerized edge runtime on industrial gateways capturing high-frequency vibration and heat signatures.
On-Device Anomaly Detection
Deployed micro-ML models evaluating sensor deviations locally without requiring continuous cloud connectivity.
Real-Time 3D Digital Twin Hub
Rendered live WebGL factory floor status enabling maintenance engineers to target failing bearings before line stops.
“Entecra turned our factory floors into intelligent, self-monitoring systems. The ROI was fully realized within the first 60 days of deployment.”
Architecture & Tech Stack
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