Edge AI · Predictive maintenance

Predict machine failures before they happen.

A model on the sensor chip reads vibration and flags faults in real time. No cloud, data stays on the factory floor.

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§01By the numbers

On the sensor, not in the cloud.

99.6%
Accuracy on CWRU Bearing
<20KB
Model in flash
<1ms
Inference time
<€10
Hardware per measuring point
§02What it detects

The faults that stop machines.

01

Rotor imbalance

Schedule balancing in good time.

02

Bearing wear

Replace the bearing before it fails.

03

Shaft misalignment

Realign the coupling at the next planned stop.

§03How it works

From vibration to firmware in four steps.

  1. Record vibration

    Accelerometer data as CSV, with fault labels.

  2. Train on Luviner

    AutoML picks the best engine. It takes seconds.

  3. Lock to hardware

    The model runs only on the sensors you register.

  4. Flash the firmware

    Download .a + .h in pure C, no dependencies.

Try it on your own vibration data.

Free Explorer plan. No credit card required.

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