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

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 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.