%0 Conference Paper %1 binanzer2023based %A Binanzer, Lisa %A Dazer, Martin %A Nicola, Andreas %B 77th STLE Annual Meeting & Exhibition %C 21.-25.05.2023, Long Beach, California (USA) %D 2023 %K a_nicola antriebstechnik from:lisabinanzer l_binanzer m_dazer myown %T AI based Prognostic and Health Management in Wind Power Drives %X Gear failure caused by pitting is one of the leading reasons of downtime in wind turbines. An adaptive operating strategy applies a load reduction of a damaged tooth by the means of torque variation to increase the remaining useful life. For the highest possible increase of service life, condition monitoring data is used to implement an AI-based prognostic and health management strategy. Measurement data from several gears with different degrees of pitting are recorded with a test gearbox. The labeled data are used to train intelligent neural networks for automatic pitting detection during operation. By using reinforcement learning and artificial intelligence it is possible to identify the degree of pitting and the time of occurrence. Different approaches and methods are investigated. Based on this, an intelligent control can be implemented. In summary, the integration of AI in the control of wind power drives enables the increase of the remaining useful life.

@inproceedings{binanzer2023based, abstract = {Gear failure caused by pitting is one of the leading reasons of downtime in wind turbines. An adaptive operating strategy applies a load reduction of a damaged tooth by the means of torque variation to increase the remaining useful life. For the highest possible increase of service life, condition monitoring data is used to implement an AI-based prognostic and health management strategy. Measurement data from several gears with different degrees of pitting are recorded with a test gearbox. The labeled data are used to train intelligent neural networks for automatic pitting detection during operation. By using reinforcement learning and artificial intelligence it is possible to identify the degree of pitting and the time of occurrence. Different approaches and methods are investigated. Based on this, an intelligent control can be implemented. In summary, the integration of AI in the control of wind power drives enables the increase of the remaining useful life.}, added-at = {2023-06-16T15:46:14.000+0200}, address = {21.-25.05.2023, Long Beach, California (USA)}, author = {Binanzer, Lisa and Dazer, Martin and Nicola, Andreas}, biburl = {https://puma.ub.uni-stuttgart.de/bibtex/264f24fc8b75c31ea1fb94980d839350a/ima-publ}, booktitle = {77th STLE Annual Meeting & Exhibition}, eventdate = {21.05.2023}, interhash = {e872201c20bf84c6424b61167dace93c}, intrahash = {64f24fc8b75c31ea1fb94980d839350a}, keywords = {a_nicola antriebstechnik from:lisabinanzer l_binanzer m_dazer myown}, month = {5}, note = {(presentation)}, timestamp = {2023-09-21T12:37:14.000+0200}, title = {AI based Prognostic and Health Management in Wind Power Drives}, year = 2023 }