  The early and robust detection of anomalies occurring in discrete
manufacturing processes allows operators to prevent harm, e.g. defects in
production machinery or products. While current approaches for data-driven
anomaly detection provide good results on the exact processes they were trained
on, they often lack the ability to flexibly adapt to changes, e.g. in products.
Continual learning promises such flexibility, allowing for an automatic
adaption of previously learnt knowledge to new tasks. Therefore, this article
discusses different continual learning approaches from the group of
regularization strategies, which are implemented, evaluated and compared based
on a real industrial metal forming dataset.
