Edge-Based Anomaly Detection for Industrial Equipment
Challenge
A manufacturing equipment company required an edge-based anomaly detection system capable of
handling 50+ sensor streams, performing TinyML inference in real-time, and
scaling across thousands of installations without relying on cloud latency.
Solution
- Real-time FreeRTOS task scheduler managing concurrent sensor acquisition, AI inference, and communication
- Optimized TinyML pipeline running on Arm Cortex-A with hardware accelerators
- Hierarchical logging with local data buffering and intelligent cloud synchronization
- Mesh networking firmware enabling multi-gateway coordination
- Robust OTA (Over-the-Air) infrastructure for deploying anomaly detection models without factory visits
Outcome
- Inference Latency: <50ms per anomaly detection cycle, meeting hard real-time requirements
- ROI Impact: Equipment downtime reduced by 40%, maintenance costs reduced by 35%
- Deployment: 5,000+ gateways deployed across North America
- Scalability: Zero firmware bottlenecks even with 10x device growth
