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, a detection of pitting damage at an early stage during operation is necessary. To investigate the detection possibilities on the test rig, a test gearbox is developed. The tooth flanks of test gears are manufactured with artificial pitting damage at different stages. The test gearbox is equipped with various load and vibration sensors mounted at different positions of the housing. The sensors acquire a large amount of data, depending on the size of the damage. The test results are used to train deep neural networks for AI based plant operation. The experiments not only show at which stage pitting damage is detectable, but also form the data basis for AI based condition monitoring of wind power drives.
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