The incorporation of advanced sensors and machine learning techniques has enabled modern manufacturing enterprises to perform data-driven in-situ quality monitoring 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 model 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 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 an additive manufacturing process, 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.

翻译：先进传感器与机器学习技术的融合，使现代制造企业能够基于制造过程中采集的传感器数据进行数据驱动的原位质量监测。然而，一个关键挑战在于：随着制造过程的持续，可能出现新型缺陷类别，导致原有机器学习模型的监测性能退化。因此，赋予机器学习模型持续学习能力的需求日益迫切。在各类持续学习方法中，基于记忆的持续学习虽性能最优，却受限于数据存储容量。为解决此问题，本文通过融合类别增量学习与过采样数据生成技术，提出一种新颖的基于伪重放的持续学习框架。该框架无需存储全部历史数据，即可在新型类别异常出现时，生成表征历史类别的高质量数据用于增量训练机器学习模型。特别地，该方法还能通过有效提升数据质量来增强监测性能。通过增材制造过程中基于监督分类的异常检测任务验证了所提框架的有效性。实验结果表明，该方法在检测新型异常方面极具潜力，同时能保持对先前任务的良好性能，并为模型架构带来更高灵活性。