  Deep learning promises performant anomaly detection on time-variant datasets,
but greatly suffers from low availability of suitable training datasets and
frequently changing tasks. Deep transfer learning offers mitigation by letting
algorithms built upon previous knowledge from different tasks or locations. In
this article, a modular deep learning algorithm for anomaly detection on time
series datasets is presented that allows for an easy integration of such
transfer learning capabilities. It is thoroughly tested on a dataset from a
discrete manufacturing process in order to prove its fundamental adequacy
towards deep industrial transfer learning - the transfer of knowledge in
industrial applications' special environment.
