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.

中文翻译：

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基于正则化的连续学习在离散制造中的异常检测

对离散制造过程中出现的异常的早期而有力的发现，使操作员可以防止伤害，例如生产机械或产品中的缺陷。尽管当前的数据驱动异常检测方法在其经过培训的确切过程中提供了良好的结果，但它们通常缺乏灵活地适应变化（例如产品）的能力。持续学习保证了这种灵活性，允许以前学习的知识自动适应新任务。因此，本文讨论了正则化策略组中不同的持续学习方法，这些方法是基于真实的工业金属成型数据集进行实施，评估和比较的。