Regularization-based Continual Learning for Anomaly Detection in Discrete Manufacturing

• URL: http://arxiv.org/abs/2101.00509v1
• Date: Sat, 2 Jan 2021 20:06:00 GMT
• Title: Regularization-based Continual Learning for Anomaly Detection in Discrete Manufacturing
• Authors: Benjamin Maschler, Thi Thu Huong Pham, Michael Weyrich
• Abstract summary: Early detection of anomalies allows operators to prevent harm, e.g. defects in production machinery or products. Current approaches for data-driven anomaly detection provide good results on the exact processes they were trained on. Continual learning promises such flexibility, allowing for an automatic adaption of previously learnt knowledge to new tasks.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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