The RWTH Aachen spin-off AEsy combines acoustic emission technology with intelligent data analysis based on machine learning, offering the first condition monitoring system for the early detection of wear and damage in plain bearings in wind turbines. The system extracts relevant condition parameters that indicate the risk of spontaneous failure in plain bearings. This enables early intervention in the plant control system, preventing spontaneous failure and extending the service life of the plain bearings and, consequently, the entire plant. AEsy offers a Condition Monitoring-as-a-Service solution that actively links continuous condition monitoring based on acoustic emission technology with the plant control system.
The 12-week HIGH-TECH.NRW Accelerator programme has proved to be extremely valuable and helpful for us. In particular, the interdisciplinary team of mentors supported us with in-depth specialist knowledge and practical expertise. We were able to pursue our goals independently, whilst at all times drawing on the strong network and engaging in dialogue with the other teams. The inspiring interactions and continuous support have significantly advanced our development. Many thanks to the team of mentors and the programme managers of the HIGH-TECH.NRW Accelerator.
Florian Wirsing
