Lisa Binanzer

Abstract

Using an autoencoder (AE) based approach enables damage detection in a very early stage and does not require labeled training data. The earliest possible damage detection in gearboxes provides a precise insight into the state of health. The aim of this investigation is to use extended training datasets that include multiple operating conditions, thus robust damage detection is investigated independent of the operating state. In a first evaluation the operating states with same speed, but different torques of a healthy gear pair are used as a training dataset. In a second evaluation, operating states of different speeds and torques are used as a training dataset. In addition, the transferability of the algorithm to other gear pairs is examined without training the AE again. The vibration data used for damage detection are recorded on the test bench with a single stage spur gearbox. The test runs are carried out at different damage sizes, speeds and torques. The AE is trained on datasets without damage. The trained AE receives datasets of the tests with and without damage and encodes them. After a subsequent decoding step, the loss of the AE is calculated. The loss varies depending on the damage size. The investigation of the extended training datasets and the transferability of the AE provides valuable insights for the further development of robust damage detection algorithms.

Abstract

Gearbox failure in offshore wind turbines can be caused by fatigue damages like tooth fank fracture (TFF) or pitting damage. For damage detection in current applications, simple threshold values for vibration amplitudes are used to initiate a shutdown of the wind turbine. At this time significant damage to the gear is already present and an external maintenance is inevitable. The objective of this experimental study is to determine a better sensor concept for detection of gear damage in wind power drives during operation. The damage needs to be detected at a very early stage before the health condition of the gears becomes critical. Evaluation criteria for comparison of sensor concepts include the earliest possible detection of damage, robust and reliable detection under varying operating conditions, as well as practicality and cost-effectiveness. The test results show, vibration measurement in radial direction and in high frequency ranges is most suitable and most robust for pitting detection during operation. With the best sensor concept in place, it is possible to detect gear damage like pitting at a very early stage of 0.3 % pitting size.

Abstract

Condition monitoring with very early gear damage detection enables remaining useful life prediction and health management of drive units. For a reliable detection of tooth flank damage during operation, a holistic approach combines experimental investigations, physical and AI-based data evaluation and is supplemented by FE and multi-body simulation.

Abstract

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.

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.

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.

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.