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.

翻译：机器学习(ML)已被广泛应用于先进制造系统中基于传感的在线监测。然而,异常状态下采集的传感器数据通常不足,导致监督式机器学习面临显著的数据不平衡问题。常见的解决方案是引入数据增强技术,即通过合成生成的方式扩充可用的异常状态数据(即少数类样本)。为生成高质量的少数类样本,学习异常状态数据的潜在分布至关重要。近年来,基于生成对抗网络(GAN)的方法在数据分布学习和数据增强方面逐渐普及。但在实际应用中,GAN数据增强所生成样本的质量可能差异显著。此外,制造系统中的传感器信号按时间顺序采集,这意味着时序信息在数据增强中也极为重要。为解决这些局限性,受多头注意力机制启发,本文提出了一种注意力堆叠GAN(AS-GAN)架构,用于制造系统在线监测的传感器数据增强。该架构采用新型注意力堆叠框架,强化GAN中生成器捕捉时序信息的能力,从而显著提升生成传感器信号的质量。随后,生成的异常状态高质量传感器信号可用于更精确地训练分类器,进一步提升制造系统的在线监测性能。在增材制造中的案例研究也成功验证了所提AS-GAN的有效性。