The equipment used for plastic paper production is highly complex. Up to 5 industrial controllers control the processes and record data from up to 400 sensors (e.g. temperatures, pressures, speeds, etc.) to be used to detect problems at an early stage and avoid production downtimes.

The sensor data was read into MATLAB® and prepared for the use of ML algorithms. From a series of different algorithms, the one with the best training results (bagged decision trees) was finally selected and applied to the machine data in production. In addition, the algorithm was integrated into the existing IT infrastructure and indicates to the operator via a user interface whether an intervention is necessary.

By using AI methods for predictive maintenance, significant savings can be achieved by avoiding production losses and machine downtime. The integration into the existing IT system allows the application to run 24/7 and supports the operating personnel in ensuring the expected production quality.