Predictive maintenance with sensor data

Equipment that fails unexpectedly costs more than equipment repaired on time. Predictive maintenance uses data to service equipment before it fails.

Collect the right signals

Depending on the equipment: temperature, vibration, current draw, pressure, run hours and fault codes.

Link to maintenance history

Predictions need examples of past failures. Record failures and repairs consistently against each asset.

Start with rules

Simple thresholds and trends — temperature rising week on week — deliver value before machine learning.

Add models when data allows

With enough history, models can estimate the likelihood of failure and remaining useful life.

Act on the predictions

Integrate alerts with maintenance planning so warnings become work orders.

See our AI and predictive analytics services.