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Arduino® UNO™ Q intranet web server helps this business run
How to achieve cost-effective predictive maintenance with the Arduino® UNO™ Q board
Most machines warn you before they fail. A motor begins to vibrate differently. A pump slowly drifts out of balance. A bearing develops a new mechanical signature. A cooling fan starts producing frequencies that were not present during normal operation.
These changes can appear well before a complete breakdown. The challenge is detecting them early, reliably, and at a cost that makes monitoring practical across more than just the factory’s most expensive machines.
UNO Q provides a flexible platform for building a compact predictive maintenance node that collects vibration data, runs a machine learning model locally, and turns unusual machine behavior into actionable alerts. The result is a practical way to begin monitoring motors, pumps, fans, bearings, compressors, and other rotating equipment without immediately deploying a complex cloud infrastructure.
Vibration is one of the most useful signals for understanding the condition of rotating machinery. When a machine is operating normally, its motor, bearings, shafts, and mechanical components produce a relatively consistent vibration pattern. Changes in alignment, balance, friction, mounting, or component wear can alter that pattern.
A traditional monitoring system might trigger an alert whenever vibration exceeds a predefined value. That approach can be useful, but machines rarely operate under perfectly fixed conditions. Speed, load, product type, temperature, mounting position, and operating mode can all influence the vibration signal. This is where anomaly detection becomes especially valuable.