The Hermle AG develops systems that record the machining center as a central parameter, providing information on the condition of the components. This information is analyzed and evaluated accordingly. This can help prevent downtime and precisely determine the need for maintenance. For a smart data analysis of the Smart Data Solution Center Baden-Württemberg (SDSC-BW) data from several machines were provided for a period of 12 months. The initial analysis focused on classifying the state of the axes of the processing center and thus identifying potentials for automated remote maintenance. The second step involved the evaluation by means of supervised learning methods (for example decision trees). The aim of the SDSC-BW experts was to use the data for the prediction of machine problems (predictive maintenance).
Through the project, the Hermle AG was able to precisely evaluate its previous approach to machine maintenance and already identify the first possibilities for improvement. In the next step, the company will incorporate these findings into their services in order to even further improve the maintenance quality: Predictions on whether and when a machine with a failure is going to be expected is optimized. This will not only benefit Hermle, but also their customers in terms of time and cost savings.
For further information, please visit: www.sdsc-bw.de
Data Innovation Community
Hermle AG, Smart Data Solution Center Baden-Württemberg
Andreas Meier, email@example.com
From May to July 2017