Regularization-based Continual Learning for Anomaly Detection in Discrete Manufacturing
Regularization-based Continual Learning for Anomaly Detection in Discrete Manufacturing
复制标题
基于正则化的离散制造异常检测持续学习
DOI：
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
M. Weyrich
中科院分区：
文献类型：
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作者：
Benjamin Maschler;T. Pham;M. Weyrich
对离散制造过程中发生的异常进行早期和可靠的检测，使操作员能够防止危害，例如生产机械或产品中的缺陷。虽然当前用于数据驱动的异常检测的方法在它们接受培训的确切流程上提供了良好的结果，但它们往往缺乏灵活适应变化的能力，例如在产品中。持续的学习保证了这种灵活性，允许将以前学到的知识自动适应新的任务。因此，本文从正则化策略组中讨论了不同的连续学习方法，并基于一个实际的工业金属成形数据集对其进行了实施、评估和比较。
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.