Autor: Lukas Egbert

This article describes a predictive maintenance approach in which a flexible sensor toolkit records and a prediction model monitors the component wear within technical systems. The condition of the components is not determined continuously, but based on time-discrete measurements. The prediction model predicts the presumable remaining useful life of the components based on the recorded data. A machine learning tool is trained with historical wear curves and used to generate the prediction. The training data is collected through statistical tests in which the influencing variables and characteristic curves of different types of wear are identified.