Philipp Häderle

Zusammenfassung

Worm gears exhibit a higher proportion of sliding motion during tooth meshing than spur gears. Typically, this type of gear consists of a soft worm wheel, often made of brass, and a hard steel worm. These characteristics promote the occurrence of abrasive wear and further fatigue damage during operation. The objective of this study is the vibration data analysis for a sensor-based detection and classification of two types of damage in worm gears during operation. For this purpose, a data set is available containing the measurement data from an accelerometer under various operating conditions with regard to rotational speed and torque. The data set comprises measurements of the undamaged condition, artificial tooth thickness reduction on the worm wheel to reflect wear, and artificial breakouts reflecting pitting damage on the worm. For damage detection the accelerometer data is evaluated in the frequency domain. The amplitudes at different frequencies are analyzed for each type of damage. Both types of damage exhibit distinct characteristics in the analyzed frequency spectra. Based on these differences, vibration-based indicators are derived from the frequency spectra, enabling the detection and classification of breakout damage on the worm and wear damage on the worm wheel. Breakout damage on the worm is characterized by a discrepancy in the acceleration amplitudes at the harmonics of the gear mesh frequency when compared to measurements obtained under undamaged conditions. For the wear damage on the worm wheel, a systematic difference compared to the undamaged reference measurement can be detected in the frequency range between 7.5 kHz and 8.5 kHz. In addition to the effect of the damage, a distinct influence of the rotational speed and torque is observed on the extracted indicators for damage detection. Since different data evaluation methods are best suited for detecting each type of damage, damage classification is possible. In the context of PHM, this enables health management measures to be implemented according to the severity of the detected type of damage.

Zusammenfassung

The dimensioning of synchronous belt drives is not standardized but based on manufacturer information and empirical values. This is partly due to a lack of fundamental knowledge about the system of a synchronous belt drive. To counteract this problem, the different failure mechanisms that can occur during the operation of a synchronous belt are investigated. Therefore, a test bench for synchronous belt drives is developed. Failure mechanisms known from the literature, such as wear, cord breakage and fabric detachment are replicated on the test bench under certain operating conditions. Especially the parameter settings of the operating conditions, where different failure mechanisms occur are further investigated. The result of this work will be a deeper understanding of the operating conditions that cause certain failure mechanisms. This deeper understanding on the failure mechanisms lays the foundation for further investigations on the service life of synchronous belt drives.

Zusammenfassung

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.

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.