Augmented Time Regularized Generative Adversarial Network (ATR-GAN) for Data Augmentation in Online Process Anomaly Detection

Supervised machine learning techniques, such as classification models, have been widely applied to online process anomaly detection in advanced manufacturing. However, since abnormal process states rarely occur in regular manufacturing settings, the data collected for model training may be highly imbalanced, which may result in significant training bias for supervised learning and, thus, further deteriorate the anomaly detection accuracy. To reduce the training bias, a natural idea is to incorporate data augmentation techniques to generate effective artificial sample data for the abnormal process states. However, most of the existing data augmentation methods do not effectively consider the temporal orders of the sensor signals, and they also usually require large amounts of actual samples to ensure satisfactory augmentation performance. To address these limitations, this article developed a novel data-driven methodology termed augmented time regularized generative adversarial network (ATR-GAN). By incorporating a proposed augmented generator, ATR-GAN is capable of generating more effective artificial samples for training supervised learning models. The novelty of this augmented generator in the proposed methodology can be summarized into three aspects: 1) an augmented filter layer is introduced in the augmented generator to identify the high-quality artificial samples; 2) in the augmented filter layer, a new distance metric termed time-regularized Hausdorff (TRH) distance is developed to accurately measure the similarity between actual samples and the generated artificial samples; and 3) batching techniques are also employed in the proposed augmented generator to further increase the diversity of the artificial data and fully utilize the relatively limited training data. In addition, the effectiveness of the proposed ATR-GAN is also validated by both numerical simulation and a real-world case study in additive manufacturing. Note to Practitioners—Online process anomaly detection currently plays a significant role in advanced manufacturing since unexpected anomalies may damage product quality and even result in catastrophic loss. In practice, processes are mostly under normal conditions, and anomalies rarely occur. Therefore, the data collected under abnormal conditions are very limited compared to normal conditions, which causes the data imbalanced issue, leading to deterioration in detection accuracy. Many existing data augmentation methods, such as generative adversarial network (GAN), cannot synthesize diversified high-quality artificial samples only using relatively limited actual samples. There is an urgent need in developing an effective data augmentation methodology to address the data imbalanced issue in process anomaly detection. This article developed a novel approach called augmented time regularized GAN (ATR-GAN) for online sensor data augmentation. With this new approach, the applications in additive manufacturing demonstrate that the performance of data augmentation can be improved effectively, and thereafter, the anomaly detection accuracy is also increased significantly. Moreover, the developed methodology is inherently integrated into a generic framework. Thus, it can be further transformed for applications in many other areas that need data augmentation.