Reino Unido
Granada, España
In this study, an application of Digital Twin concept is explored to foster intelligentmaintenance strategies and enhance the reliability of wind turbines amidst their rapid industrygrowth. It introduces a Petri net-based model that integrates condition monitoring (CM) andpredictive maintenance processes for managing wind turbine blade assets effectively. Theprocess begins with simulating blade degradation scenarios, followed by the inclusion of acondition monitoring system that consistently identifies the state of a blade, considering bothpotential underestimations and overestimations of the actual state. Based on these identifiedstates, engineers can select appropriate maintenance strategies. By introducing Petri Net (PN)modules, our model provides detailed predictions of the health of wind turbine blades undervarious asset management strategies. The proposed Digital Twin model offers a means toextend the lifespan of a blade in a cost-effective way.
© 2008-2025 Fundación Dialnet · Todos los derechos reservados