Predictive Maintenance Edge Node (IIoT)
An intelligent industrial sensor node that uses on-device Edge AI to detect motor anomalies 2 weeks before failure occurs.
Technical Specifications
| Parameter | Specification |
|---|---|
| MCU | STM32N6 (Arm Cortex-M55 + Ethos-U55 NPU) |
| AI Performance | 600 GOPS (Int8) |
| Sensors | 3-Axis Accelerometer (20kHz bandwidth), Temperature |
| Connectivity | Industrial Ethernet (Profinet), WirelessHART |
| Security | IEC 62443-4-2 SL3 Compliant, Secure Boot |
| Power | 24V DC / PoE (Power over Ethernet) |
The Challenge
An automotive manufacturing plant faced €50k/hour losses due to sudden robotic arm failures. Cloud-based monitoring solutions had too much latency and high data transmission costs. They needed a “sensor-to-action” loop of under 100ms.
Our Solution
We developed a dedicated Edge AI Node capable of running vibration analysis models directly on the sensor:
- TinyML Inference: Compressed YOLOv8-nano and custom 1D-CNN models running on the STM32 NPU.
- Anomaly Detection: Unsupervised learning (Autoencoders) to detect unknown failure modes.
- Local Control: Direct integration with PLC via Profinet to trigger emergency stops in < 10ms.
Impact
- 73% Reduction in unplanned downtime within 6 months.
- €40k Annual Savings per production line in data transmission costs (only anomalies are uploaded).
- 100% Privacy: Raw vibration data never leaves the factory floor.