基于伪重放的类持续学习在先进制造在线新类别异常检测中的应用
Pseudo Replay-based Class Continual Learning for Online New Category Anomaly Detection in Advanced Manufacturing
AI总结：
本文提出一种基于伪重发的类持续学习框架，通过整合类增量学习和过采样数据生成，解决先进制造中新类别异常导致的模型性能下降问题，在不存储所有数据的情况下实现高质量数据生成与增量训练，有效检测新异常并保持旧任务性能。
摘要：
先进传感器和机器学习技术的引入，使现代制造企业能够基于制造过程中收集的传感器数据，执行数据驱动的基于分类的异常检测。然而，一个关键挑战是，随着制造过程的继续，可能会出现新的缺陷类别，导致先前训练的机器学习模型的监控性能下降。因此，越来越需要赋予机器学习模型持续学习的能力。在所有持续学习方法中，基于内存的持续学习性能最好，但面临数据存储容量的限制。为了解决这个问题，本文通过整合类增量学习和基于过采样的数据生成，开发了一种新颖的基于伪重放的持续学习框架。在不存储所有数据的情况下，开发的框架能够生成代表先前类别的高质量数据，以在新类别异常发生时增量地训练机器学习模型。此外，它甚至可以增强监控性能，因为它也有效地提高了数据质量。所提出框架的有效性在三个案例研究中得到了验证，这些研究利用监督分类问题进行异常检测。实验结果表明，所开发的方法在检测新异常的同时保持对先前任务的良好性能方面非常有前景，并为模型架构带来了更多灵活性。
英文摘要：
The incorporation of advanced sensors and machine learning techniques has enabled modern manufacturing enterprises to perform data-driven classification-based anomaly detection based on the sensor data collected in manufacturing processes. However, one critical challenge is that newly presented defect category may manifest as the manufacturing process continues, resulting in monitoring performance deterioration of previously trained machine learning models. Hence, there is an increasing need for empowering machine learning models to learn continually. Among all continual learning methods, memory-based continual learning has the best performance but faces the constraints of data storage capacity. To address this issue, this paper develops a novel pseudo replay-based continual learning framework by integrating class incremental learning and oversampling-based data generation. Without storing all the data, the developed framework could generate high-quality data representing previous classes to train machine learning model incrementally when new category anomaly occurs. In addition, it could even enhance the monitoring performance since it also effectively improves the data quality. The effectiveness of the proposed framework is validated in three cases studies, which leverages supervised classification problem for anomaly detection. The experimental results show that the developed method is very promising in detecting novel anomaly while maintaining a good performance on the previous task and brings up more flexibility in model architecture.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
