  Machine learning (ML) has been extensively adopted for the online
sensing-based monitoring in advanced manufacturing systems. However, the sensor
data collected under abnormal states are usually insufficient, leading to
significant data imbalanced issue for supervised machine learning. A common
solution is to incorporate data augmentation techniques, i.e., augmenting the
available abnormal states data (i.e., minority samples) via synthetic
generation. To generate the high-quality minority samples, it is vital to learn
the underlying distribution of the abnormal states data. In recent years, the
generative adversarial network (GAN)-based approaches become popular to learn
data distribution as well as perform data augmentation. However, in practice,
the quality of generated samples from GAN-based data augmentation may vary
drastically. In addition, the sensor signals are collected sequentially by time
from the manufacturing systems, which means sequential information is also very
important in data augmentation. To address these limitations, inspired by the
multi-head attention mechanism, this paper proposed an attention-stacked GAN
(AS-GAN) architecture for sensor data augmentation of online monitoring in
manufacturing system. It incorporates a new attention-stacked framework to
strengthen the generator in GAN with the capability of capturing sequential
information, and thereby the developed attention-stacked framework greatly
helps to improve the quality of the generated sensor signals. Afterwards, the
generated high-quality sensor signals for abnormal states could be applied to
train classifiers more accurately, further improving the online monitoring
performance of manufacturing systems. The case study conducted in additive
manufacturing also successfully validated the effectiveness of the proposed
AS-GAN.
