s.maintenance

Thanks to our data-driven software s.maintenance, we can not only make precise predictions about the remaining service life of wear components, but also maximise it and initiate maintenance activities early. Thus, s.maintenance helps achieve a more efficient use of resources and cut maintenance costs. Our predictive maintenance software takes into account the actual wear and predicts component wear before any machine failure can occur. This allows you to keep a permanent eye on the current functional state of your plant or machine, contributes to a more effective spare parts management and significantly reduces and optimises your service/maintenance intervals and activities. The wear and tear of parts mainly depends on factors such as process parameters and intensity of use. Applying a machine learning approach, the algorithm of s.maintenance uses this information to create a precise model describing the wear-out dynamics of a specific component. On this basis, it can make precise predictions as to when critical usage or failure limits are reached. The result is a continuous, intelligent and automatic monitoring system for relevant wear components that is used to plan and create a predictive maintenance schedule for the machine or an entire production line.