Pseudo Replay-based Class Continual Learning for Online New Category
  Anomaly Detection in Additive Manufacturing
Zhangyue Shi Tianxin Xie Chenang Liu Yuxuan Li
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.