  Due to its probabilistic nature, fault prognostics is a prime example of a
use case for deep learning utilizing big data. However, the low availability of
such data sets combined with the high effort of fitting, parameterizing and
evaluating complex learning algorithms to the heterogenous and dynamic settings
typical for industrial applications oftentimes prevents the practical
application of this approach. Automatic adaptation to new or dynamically
changing fault prognostics scenarios can be achieved using transfer learning or
continual learning methods. In this paper, a first survey of such approaches is
carried out, aiming at establishing best practices for future research in this
field. It is shown that the field is lacking common benchmarks to robustly
compare results and facilitate scientific progress. Therefore, the data sets
utilized in these publications are surveyed as well in order to identify
suitable candidates for such benchmark scenarios.
