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Machine learning (ML) has been extensively adopted for the onlinesensing-based monitoring in advanced manufacturing systems. However, the sensordata collected under abnormal states are usually insufficient, leading tosignificant data imbalanced issue for supervised machine learning. A commonsolution for this issue is to incorporate data augmentation technique, i.e.,augmenting the available abnormal states data (i.e., minority samples) viasynthetic generation. To generate the high-quality minority sampleseffectively, it is vital to learn the underlying distribution of the abnormalstates data. In recent years, the generative adversarial network (GAN)-basedapproaches become popular to learn data distribution as well as perform dataaugmentation. However, in practice, the quality of generated samples fromGAN-based data augmentation may vary drastically. In addition, the sensorsignals are collected sequentially by time from the manufacturing systems,which means the consideration of sequential information is also very importantin data augmentation. To address these limitations, inspired by the multi-headattention mechanism, this paper proposed an attention-stacked GAN (AS-GAN)architecture for the sensor data augmentation of online monitoring in advancedmanufacturing. In this proposed AS-GAN, a new attention-stacked framework isincorporated to strengthen the generator in GAN with the learning capability ofconsidering sequential information. Furthermore, the developedattention-stacked framework also greatly helps to improve the quality ofgenerated sensor signals. The case studies conducted in additive manufacturingalso successfully validate the effectiveness of AS-GAN to augment high-qualityartificial multi-channel sensor signals for online monitoring of manufacturingsystems.

Attention-stacked Generative Adversarial Network (AS-GAN)-empowered Sensor Data Augmentation for Online Monitoring of Manufacturing System