In this project, concepts and methods for detecting and predicting the energy efficiency of vacuum systems and pneumatic drives are being developed and implemented directly on customer systems.


The efficient use of resources and energy in industry is a key factor in achieving global climate protection targets. As significant losses in energy efficiency only occur during operation due to wear and tear effects, changes in use or fault conditions such as compressed air leaks, there are large deviations between expected and actual energy consumption. This is why the use of current system data is essential in order to take action at an early stage with measures such as conversions or adaptation strategies. Only in this way can the savings measures predicted during the planning phase be secured in the long term. System and plant monitoring is therefore a measure to ensure efficiency in terms of resources and emissions. Furthermore, in the field of vacuum technology, the detection of faults and anomalies that occur during operation and the resulting adaptations play an essential role in ensuring energy-efficient and process-safe handling without component loss.

In order to cope with the large amount of sensor data and the complexity of automated production lines, data-based artificial intelligence learning methods are to be used. Consumption monitoring and the detection and diagnosis of faulty conditions as part of the predictive maintenance concept are important measures here. The needs-based design of vacuum and drive systems has a significant impact on energy efficiency. For this reason, the focus is also on optimizing drive systems, vacuum generators and their architecture as well as their operating strategies. 

J. Schmalz GmbH

Project partners


This project is funded by the Federal Ministry for Economic Affairs and Climate Protection.


This image shows Caroline Trage

Caroline Trage


Research Assistant

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