Batch #6

Category: Production & Process Engineering

The spin-off AEsy from RWTH Aachen University combines acoustic emission technology with intelligent data evaluation based on machine learning and offers a condition monitoring system for the first time for the early detection of wear and damage of plain bearings in wind turbines. Relevant condition parameters that indicate spontaneous failure of plain bearings are extracted. In this way, the system control system can be intervened at an early stage, spontaneous failure can be prevented and the service life of the plain bearings and thus the entire system can be extended. AEsy is a condition monitoring-as-a-service solution that actively links continuous condition monitoring based on acoustic emissions technology with plant control.