  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 therefore not
the relay itself, but the reduced machine availability. It is therefore
desirable to predict relay degradation as accurately as possible for specific
applications in order to be able to replace relays in good time and avoid
unplanned machine downtimes. Nevertheless, data-driven failure prediction for
electromechanical relays faces the challenge that relay degradation behavior is
highly dependent on the operating conditions, high-resolution measurement data
on relay degradation behavior is only collected in rare cases, and such data
can then only cover a fraction of the possible operating environments. Relays
are thus representative of many other central standard components in automation
technology.
