Kürbiskernerntemaschine: Optimierung Verschleißteilwartung Wälzlager und Effizienzsteigerung

Abstract

This master’s thesis is dedicated to the analysis and optimization of the AXR 430-2023 pumpkin seed harvester from AGRO-STAHL Agrartechnik & Stahlbau GmbH. In an increasingly digitalized environment, data about the machines is collected, especially with regard to wear part maintenance. As the pumpkin seed harvest is highly dependent on weather conditions, early identification of wear is crucial to maximize harvest performance and minimize downtime. The thesis aims to identify faults and damage to the machines during the pumpkin seed harvest in order to carry out appropriate repairs out of season. The integration of machine data analysis and forecasting plays a crucial role here, especially for the prediction of potential bearing failures. The main objective is to optimize harvesting efficiency and thus reduce costs and effort for farmers. At the end a recommendation for action will be developed and a calculation of the bearing service life will be carried out using recorded machine data. These analyses are intended to provide insights into improving the service and a possible forecast of future bearing wear part failures. The results of the calculations show that it is not possible to accurately predict the time of bearing failure based on speed measurements alone. Nevertheless, the analyses indicate that certain bearings, particularly in the cleaning unit, are exposed to an increased risk of premature failure. However, further parameters such as temperature, bearing noise and vibrations are required for a reliable prognosis. The thesis is divided into different chapters, starting with an introduction to the pumpkin seed machine and its assemblies, followed by an explanation of basic concepts in the area of bearings and wear. The installed bearings are then examined in more detail and various maintenance strategies are presented. Finally, a recommendation for action to increase performance is calculated and the bearing service life is analyzed and calculated based on the rotational speeds

Date of Award	2024
Original language	German (Austria)
Supervisor	Michael Schmidthaler (Supervisor)