Adaptive Damage Accumulation and Remaining-Service-Life-Prediction for Gearboxes

• Applicant: Professor Dr.-Ing. Karsten Stahl
• Subject Area: Engineering Design, Machine Elements, Product Development
• Term: from 2020 to 2024
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 448253450

Most machines and machine elements operating today are designed for a limited service-life. On the one hand, 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 probability of a failure increases. 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, there is very little possibility to validate the technical design during operation at the moment. 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 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. Experimental investigations are planned to validate a remaining-service-life-prediction, mostly based on machine learning and to investigate the opportunities of machine learning for the traditional 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.

• DFG Programme: Research Grants