Martin Dazer

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

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.

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.

Abstract

Modern vehicles have increasing safety requirements and a need for reliable low-voltage power supply in their on-board power supply systems. Understanding the causes and probabilities of failures in a 12 V power supply is crucial. Field analyses of aged and failed 12 V lead batteries can provide valuable insights regarding this topic. In a previous study, non-invasive electrical testing was used to objectively determine the reasons for failure and the lifetime of individual batteries. By identifying all of the potential failure mechanisms, the Latin hypercube sampling method was found to effectively reduce the required sample size. To ensure sufficient confidence in validating diagnostic algorithms and calculating time-dependent failure rates, all identified aging phenomena must be considered. This study presents a probability distribution of the failure mechanisms that occur in the field, as well as provides insights into potential opportunities, but it also challenges diagnostic approaches for current and future vehicles.

Abstract

An empirical-statistical demonstration of the lifetime of engineering products is necessary in order to assure fatigue and reliability requirements 1. Engineers are faced with the challenge of selecting the most suitable test strategy and also the optimal parameter setting, e.g. sample size, in order to realize reliability demonstration with limited costs, time and with their available testing resources. In this paper, different test strategies such as Sudden Death, time-censored, failurecensored and non-censored tests, and the Success Run Test are compared in a Reliability DOE concerning the necessary time, costs and the Probability of Success of the whole test configuration. The DOE is carried out with standardized cost and time data and the results can therefore be used generally and are transferable to any other product. With the resulting data, the different test strategies are comparable in terms of different boundary conditions. The Probability of Success can be determined and compared for the different strategies with a very low computing time in terms of cost and time. Using these results, engineers are now able to select the most suitable test procedure for their individual reliability requirements. Finally, the applicability is presented in a case study, showing the difference between different test strategies for the demonstration of a specific reliability target.

Abstract

If the focus during product development is on estimating a failure distribution function or a lifetime-quantile, analysis of failure behavior or determination of optimal design alternatives, there is often no alternative to life tests until failure. Alongside the determination of sample size for a desired accuracy of the result, engineers are also faced with the challenge of selecting the most suitable life test type at limited cost, time and test rig capacities. In this paper, results of a comprehensive simulation study are presented. Sudden Death tests, type-I and type-II censored and non-censored life test types are compared for two-parameter Weibull distributed data with respect to accuracy, time, cost and the investigation target. Significant influencing factors and boundary conditions, such as the maximum number of test rigs available, are taken into account to assure the practical applicability of the procedure for planning life tests. The database generated provides recommendations for an appropriate test configuration within an accuracy, time and cost triangle, including optimal test parameters. Furthermore, it enables engineers to substantiate qualitatively known correlations with numbers. This information can be used to describe the influence of a shift in boundary conditions. Finally, the use of the simulation results as basis for planning life tests is illustrated in a case study.