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Deep learning promises performant anomaly detection on time-variant datasets,but greatly suffers from low availability of suitable training datasets andfrequently changing tasks. Deep transfer learning offers mitigation by lettingalgorithms built upon previous knowledge from different tasks or locations. Inthis article, a modular deep learning algorithm for anomaly detection on timeseries datasets is presented that allows for an easy integration of suchtransfer learning capabilities. It is thoroughly tested on a dataset from adiscrete manufacturing process in order to prove its fundamental adequacytowards deep industrial transfer learning - the transfer of knowledge inindustrial applications' special environment.

Towards Deep Industrial Transfer Learning for Anomaly Detection on Time Series Data