Binanzer, Lisa ; Merkle, Lukas ; Dazer, Martin ; Nicola, Andreas: Pitting Detection for Prognostics and Health Management in Gearbox Applications. In: Proceedings of the International Conference on Gears 2023. 13.-15.09.2023, Garching/Munich, 2023, S. 97–108
Abstract
In critical industrial applications, gearboxes are already equipped with Condition Monitoring Systems (CMS) based on vibration sensors. However, these systems are only developing their full potential if damages can be detected early and if the accumulated sensor data can be assigned to a type of damage and a damage size. The aim of this investigation is a systematic evaluation of gear damages at different stages to use the CMS sensor data for Prognostics and Health Management (PHM). It is also the objective to evaluate different sensor concepts for data acquisition regarding damage detection in gearbox applications. To investigate the detection possibilities on the test bench, a single stage spur gearbox is developed. The test gearbox is used on a test bench, which is designed as electrical load unit consisting of two electric motors. The tooth flanks of the test gears are manufactured with artificial pitting dam-age with different sizes. The test gearbox is equipped with torque, speed and vibration sen-sors. The experiments are statistically designed by means of Design of Experiments (DOE). The factors pitting damage size, rotational speed, torque and viscosity are varied. This offers a valid basis for developing an empiric model that relates the damage size to the observed sen-sor data. Using machine learning approaches, the vibration signal data are analyzed. By means of anomaly detection, early damage stages can be identified during operation. A con-clusion from the acquired sensor data on the extent of damage is possible through the sys-tematic DOE. This enables PHM which allows a more reliable and sustainable operation of drivetrains in industrial plants and systems.Binanzer, Lisa ; Dazer, Martin ; Nicola, Andreas: AI based Prognostic and Health Management in Wind Power Drives. In: 77th STLE Annual Meeting & Exhibition. 21.-25.05.2023, Long Beach, California (USA), 2023 — IMA-ZUV (presentation)
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.Merkle, Lukas ; Dazer, Martin ; Nicola, Andreas: Pitting Detection in an Early Damage Stage for AI Based Operating Strategies in Wind Power Drives. In: 77th STLE Annual Meeting & Exhibition. 21.-25.05.2023, Long Beach, California (USA), 2023 — IMA-ZUV (presentation)
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.Merkle, Lukas ; Binanzer, Lisa ; Dazer, Martin ; Nicola, Andreas: Artificial Intelligence for Sustainable Control of Wind Power Drives. In: Proceedings of CWD 2023 - Conference for Wind Power Drives. 21.-22.03.2023, Eurogress, Aachen, 2023, S. 172–180
Abstract
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.
