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 provide good results on the exact they were trained on, often lack ability flexibly adapt changes, products. Continual learning promises such flexibility, allowing an automat...