Adaptive Remanufacturing – Decision Model for the Intelligent Maintenance of Production Resources

The life cycle extension of capital goods holds considerable ecological and economic potential in terms of improved resource efficiency in view of increasing resource scarcity and rising environmental awareness. The innovative approach of Adaptive Remanufacturing (AdR) allows to raise these potentials. The objective of this article is the detailed introduction of the structure and functioning of the AdR decision model. AdR describes a proactive, intelligent maintenance strategy based on structured decisions about the optimal time and scope of maintenance and remanufacturing measures. The basis of assessment are the remanufacturing-relevant components of a capital good. To protect these components from critical wear and tear, component conditions are monitored, and sensor measurement data are transferred into the decision model as dynamic input. Based on condition monitoring and defined threshold values per sensor, critical states of the monitored components are predicted. These forecasts trigger the decision process to identify and suggest suitable measures and the optimal point in time for their application based on a catalogue of measures, which serves as static input for the decision model. From this catalogue, feasible measures are preselected according to their technical suitability for specific components and wear mechanisms. To determine the optimal measure for a given component and wear, measures are assessed in terms of benefits regarding the state of a component as well as economic and ecological efforts. The decision model is implemented in a web-based software application. As a main result, it provides key figures regarding short- and long-term expenses, the CO2 consumption, and the effective duration per measure. Moreover, a prioritised measure recommendation is issued. The AdR decision model thus enables the substantiated selection of the optimal maintenance or remanufacturing measure per component.