Lisa Binanzer

Zusammenfassung

Many failures of wind turbines are due to drive failures caused by pitting. Each failure can be associated with high repair costs and time-consuming repair work. This particularly applies to offshore facilities. For these reasons, increasing the remaining useful life of wind power drives is essential to leave a minimal ecological footprint by simultaneously increasing power output. Pitting damage occurs first on the weakest tooth. Artificial Intelligence is used to apply a local load reduction to a pre-damaged tooth and delay degradation. The other intact teeth compensate for the load reduction in order to achieve a constant average power. To increase the service life of wind power drives and to avoid unexpected failures an adaptive operating strategy can be implemented. With a test gearbox the adaptive operating strategy is examined on a test bench. The test gearbox is equipped with test gears with varying degrees of pre-damage. The objective of the examinations on the test gearbox is to detect pitting damage at the earliest possible stage. The earlier damage is detected, the greater the potential for increasing useful life. For detection, multiple high frequency acceleration sensors are integrated in the gearbox. Using machine learning approaches, the vibration data are analyzed. By means of anomaly detection damage can be identified during operation. Using torque control on the test bench, the load on pre-damaged teeth is minimized depending on the detected damage. In summary, the findings on the test gearbox will provide fundamental knowledge that will enable the implementation of the adaptive operating strategy inwind power drives.