Edge AI Embedded Systems for Predictive Maintenance AI IoT: Low-Latency Vision for Conveyor Anomaly Detection
Introduction: The Challenge of Instantaneous Safety Response
Manufacturing lines require instantaneous anomaly detection and automated shutdown or actuation. Cloud processing incurs latency and network dependency that is unacceptable for safety-critical responses. The business need was a vision system to detect jams and foreign objects with deterministic low latency and high reliability, a core requirement for any smart infrastructure solution.
Solution Overview: Edge AI Embedded Systems for Sub-200 ms Anomaly Detection
EurthTech delivered an on-device computer vision pipeline that detects anomalies (blockages, misfeeds, spillages) in conveyor lines with sub-200 ms end-to-end latency. The Edge AI embedded system uses a local
TPU or small GPU accelerator for real-time inference. When an anomaly is confirmed, the node immediately triggers a local hardware interlock, ensuring immediate automated gating and aligning perfectly with a Predictive maintenance AI IoT strategy.
Technical Implementation: Embedded Systems Development and AI Engineering
The embedded systems development utilized a two-stage pipeline to reduce compute: a lightweight motion detector triggers a compact CNN classifier (MobileNetV2/pruned\ResNet) for anomaly classification. We achieved an inference latency under 25ms through model quantisation and pruning. To meet safety timing, the pipeline used hard real-time scheduling and a redundant watchdog logic requiring two independent anomaly confirmations within 100ms. This showcases our advanced AI engineering solutions.
Results & Impact: AI Engineering and Operational ROI
Bench tests measured a detection TPR of 0.96 and FPR < 0.02. Field trials across three lines significantly reduced manual stops by 67% and prevented material spill damage, with an estimated savings of 18% in line downtime. The system also produced a timestamped event log for root-cause analysis, demonstrating a high ROI solution for automated manufacturing.
Scalability & GIS Context: The solution integrates with PLCs/SCADA and provides escalation flows. By creating a deterministic, local Vision node, this system acts as a key component of the factory's Digital twin smart city model, providing the ground truth data for OEE and flow optimization. This capability is a natural extension of our GIS consulting company expertise into the industrial indoor environment.