Posters

Abstract

All maintenance enhancing systems share the common goal of early damage detection and minimizing downtime.andincludesoperation and maintenance.In the event thata wind turbine is inactive for maintenance, an alternative power plant must supply the required electricity.Looking atthe present energymixand the urgency upon unplanned downtime,thesubstitutingpower plantis most of the time powered with fossil fuels. Therefore, the use of next generation condition monitoring can enable prescriptive maintenance,which is the key to sustainable operation and maintenance. We present the development ofa new technologyfor intelligent wear detection tomonitorwear particles and dangerous degradation of critical powertrain components. The technology uses magnetic effects and enables both detection and automatic characterization of wear particles during operation.This unlocksnew datathat is a direct indicator ofcomponentwear. Inconjunction with machine learningweprovidedata for risk assessment of wear particles and their assignment to specificcomponentgroups.Specific information about theoverall stateof the gearboxcan then beusedin real-timefor a truly prescriptive maintenanceapproach.The sensor system works at any rotational speed of components and flow velocity of wear particles as well as with any complexity of assembly.Thus, the sensor can be used for planetary stagesandcan be implemented in oil bath-lubricated applications.