Arduino has published a technical guide detailing how its Arduino UNO Q board can serve as a self-contained predictive maintenance node for rotating industrial equipment, positioning the platform as an entry point into edge ML monitoring that does not require cloud infrastructure for core fault detection.
The board's dual-architecture design splits the workload: a microcontroller side handles sensor acquisition while a Linux side manages data logging, model execution, and local dashboards. A Modulino Movement sensor mounted on a motor or pump housing captures acceleration data across three axes, building a time-series record of direction, amplitude, and frequency. That signal feeds a machine learning model running locally on the UNO Q's NPU, which produces an anomaly score when the vibration pattern deviates from learned baseline behavior.
What's new here is the combination of sensing, local inference, Linux-based dashboards, connectivity, and machine-facing control outputs on a single platform. Engineers working with limited fault data can train anomaly detection models, which identify deviations from normal behavior without needing labeled examples of every failure mode. Teams with existing fault libraries can use classification instead, matching incoming signals against known conditions such as imbalance, misalignment, or loose mounting. Arduino App Lab ships sample applications covering both approaches.
When an anomaly score exceeds a validated threshold, the system can activate a warning light, sound a buzzer, log the event, or send a message to a maintenance service. Because detection runs locally, the core alert loop operates without a live network connection. Cloud connectivity can still be added for aggregating data across multiple machines, but it is not required for the system to function. For constrained deployments, Edge Impulse provides quantized int8 models and RAM-optimized builds suited to always-on edge monitoring.
The UNO Q is available through the Arduino Store and distributors including DigiKey, Farnell, Mouser, Newark, and RS Components. The board can run alongside existing industrial control systems without interfering with them, which reduces the barrier for teams looking to layer predictive monitoring onto equipment already in production.
