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Electronics & IoT

Predictive maintenance: what vibrations tell us

How sensors and AI models anticipate failures of motors, pumps and bearings, starting from machines already installed.

Predictive maintenance: what vibrations tell us

Every rotating machine has its own vibration "signature". When something starts to deteriorate, the signature changes long before the failure becomes visible.

The model learns normal behaviour and flags deviations.
The model learns normal behaviour and flags deviations.

What is measured

An accelerometer mounted close to the bearings measures vibration; a current sensor measures the motor's draw; a temperature sensor completes the picture. Imbalance, misalignment and bearing wear leave different traces in these measurements.

From sensor to model

The device processes measurements locally and computes compact indicators. An anomaly detection model learns the machine's normal behaviour in the first weeks and from then on flags deviations, without needing recorded examples of failures.

How to introduce it in a company

  1. Start with a few critical machines, those whose downtime costs the most.
  2. Install the sensors without modifying the machine.
  3. Compare alerts with the maintenance team's actual interventions.
  4. Extend to other machines once the system has proven its value.

The advantage is not just avoiding failures: it is scheduling interventions at the right time and ordering spare parts before they are needed.

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