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Stuttgart Open Relay Degradation Dataset (SOReDD) by Maschler, Benjamin; Iliev, Angel; Pham, Thi Thu Huong; Weyrich, Michael is a scholarly article available to read on EtoBox.

What is Stuttgart Open Relay Degradation Dataset (SOReDD) about?

Real-life industrial use cases for machine learning oftentimes involve heterogeneous and dynamic assets, processes and data, resulting in a need to continuously adapt the learning algorithm accordingly. Industrial transfer learning offers to lower the effort of such adaptation by allowing the utilization of previously acquired knowledge in solving new (variants of) tasks. Being data-driven methods, the development of industrial transfer learning algorithms naturally requires appropriate datasets for training. However, open-source datasets suitable for transfer learning training, i.e. spanning different assets, processes and data (variants), are rare. With the Stuttgart Open Relay Degradation Dataset (SOReDD) we want to offer such a dataset. It provides data on the degradation of different electromechanical relays under different operating conditions, allowing for a large number of different transfer scenarios. Although such relays themselves are usually inexpensive standard components, their failure often leads to the failure of a machine as a whole due to their role as the central power switching element of a machine. The main cost factor in the event of a relay defect is therefor

Author

Maschler, Benjamin; Iliev, Angel; Pham, Thi Thu Huong; Weyrich, Michael

Published

2022

Language

EN