Vario-Up predictive CVT maintenance
The availability, reliability, and cost-effectiveness of tractors are decisive factors that not only influence farmers’ efficiency, but also have a lasting impact on the responsible use of resources. In order to prevent downtime, predictive maintenance on CVT transmissions will make sense. In a project, conducted by the Technical University of Munich in Germany (TUM) and […]
The availability, reliability, and cost-effectiveness of tractors are decisive factors that not only influence farmers’ efficiency, but also have a lasting impact on the responsible use of resources. In order to prevent downtime, predictive maintenance on CVT transmissions will make sense.
In a project, conducted by the Technical University of Munich in Germany (TUM) and named “Vario-Up”, the transmission is considered a central topic, due to its key role in the performance and durability of the tractor.
Vario-Up aims to develop advanced models and digital tools for assessing and forecasting the health and Remaining Useful Life (RUL) of key transmission components in agricultural tractors. By leveraging state-of-the-art sensors, machine learning, and digital twin technologies, the project aims to predict the RUL of:
– the transmission oil filter
– the transmission oil itself
– the overall transmission unit
The project is funded by the “Bayerische Transformations- und Forschungsstiftung” and supported by AGCO Fendt.
This predictive insight will form the foundation for predictive maintenance strategies that help reduce downtime, improve operational reliability, and support more sustainable and resource-efficient use of agricultural machinery, bringing tangible value to both farmers and industry. To create the prediction models, advanced sensors are used, which are installed both in real tractors and on the transmission test bench. These sensors provide valuable data that is processed using innovative modeling approaches such as machine learning and digital twins to enable accurate predictions about the condition of the transmissions and their components.
The result will be a sound basis for predictive maintenance that optimizes operations, reduces downtime, and at the same time contributes to more sustainable use of resources in agriculture.
The project officially kicked off with a handover event attended by State Secretary Tobias Gotthardt, together with representatives from the Bayerische Transformations- und Forschungsstiftung, Lukas P. and Prof. Timo Oksanen (TUM), and AGCO Fendt.
