Research Topic
| Short Title | Remaining-Service-Life-Prediction |
| Start of Project | Q4/2020 |
| Funding | DFG-Nr. 448253450, STA 1198/19-1 German Research Foundation, DFG |
| Contact | Dr.-Ing. J. Pellkofer |
Project Description
Most machines and machine elements operating today are designed for a limited service-life. This creates significant potential for increasing efficiency, decreasing cost and reduction of the carbon-footprint. On the other hand, this causes a rising risk of failure with increasing age of the machine. Especially if a failure can cause serious damage to humans or the environment or can result in a high economic loss, this fact creates a conflict of goals. On the one hand, the machine should only be maintained or replaced when necessary and on the other hand, the increasing probability of a failure creates a serious risk. Therefor a method is desirable, making it possible to predict the remaining service-life and state of health with as little effort as possible.
Gearboxes are an elementary component of many technical systems, for example robots, wind turbines and cars with electric or combustion engine. Centerpiece of gearboxes are the gears. A failure of these components usually causes the whole gearbox to fail. The fatigue life analysis deals with the dimensioning of gears according to the expected loads and the required service-life. Unfortunately, now there is very little possibility to validate this technical design during operation. Hence, the goal of this research proposal is to create a method, enabling the prediction of the remaining-service-life and state-of-health of gears during the operation. It is planned to design the method in a way, enabling an easy transfer of the results to other machine elements.
Because of the increasing spread of sensors in machines, more and more operating-data is recorded. One goal of this research proposal is to investigate the potential of analyzing this data with big-data- and machine learning methods. Experiments are planned to validate a remaining-service-life-prediction, mostly based on machine learning. This is intended to investigate the opportunities of machine learning for the fatigue life analysis. Finally, a remaining-service-life-prediction created according to these principals can reduce waste of resources and can furthermore increase the safety of machines.