Forschungsprojekt: Lebensdauersteigerung Grübchen

Zusammenfassung

The economic efficiency of machinery operation is significantly impacted by maintenance strategies. In the realm of condition-based or predictive maintenance strategies, the early detection of fatigue-induced damages is crucial. Therefore, this study focuses on the early detection of pitting damages during operation. Experimental investigations are conducted on a test gearbox acquiring acceleration data for different sizes of pitting damages under diverse operating conditions. A successful detection of the pitting damages during operation is achieved at a very early stage of progression, with a minimum size of 0.41% of an active tooth flank area. The utilization of design of experiments techniques facilitates the identification of factors influencing the detectability of pitting damages. The obtained results are analysed to elucidate the physical basis for the reliable detection of pitting damages across diverse operating conditions.

Zusammenfassung

Early damage detection in gearbox applications enables the implementation of Prognostics and Health Management (PHM). On the one hand, the earliest possible damage detection provides a precise in-sight into the state of health of a gearbox. In addition, early damage detection offers the possibility to slow down the damage progress and extend the remaining useful life (RUL) by intervening in the operating state at an early damage stage. The main contribution of this work is that existing Machine Learning tools are applied to the challenge of very early damage detection in gearboxes. Thus, the need for complex physically based data evaluation is avoided. The aim of this investigation is a comparison of two different machine learning approaches. To investigate the detection possibilities test bench experiments were conducted with a single stage spur gearbox. For a comprehensive investigation, i.e. to detect damage under different operating conditions, the test runs are carried out at different damage sizes, speeds and torques. Based on the recorded vibration data, the damage detection is examined. Two machine learning approaches of anomaly detection are considered: An encoding approach and a loss approach. The same sparse autoencoder is developed for both approaches Both machine learning approaches are able to detect even the smallest damage of about 0.5 % in most operating states. The loss approach allows the different damage stages to be recognized much more clearly than the encoding approach. The comparison of the different approaches provides valuable insights for the further development of robust damage detection algorithms.

Zusammenfassung

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.

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.