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

arXiv:2306.06268v2 Announce Type: replace
Abstract: 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 …

abstract advanced adversarial arxiv attention augmentation cs.lg data gan generative generative adversarial network issue machine machine learning manufacturing monitoring network sensing sensor solution supervised machine learning systems type