If the monitoring data is collected on an Industrial Internet of Things (IIoT) platform, a machine history can be built up over time. Comprehensive platforms such as the FIELD system from Fanuc are ideal for this purpose. Or locally based solutions such as the i.Cee:local from igus. This allows to advance the supervised learning on the machine even further: if trends have been observed over a longer period of time, predictions can be made about component wear. This is the step towards predictive maintenance. What’s more, the history can be used to observe whether certain process parameters promote wear, in which case the processes can be adjusted accordingly.