Data augmentation for numerical data from manufacturing processes: an overview of techniques and assessment of when which techniques work

Over the past two decades, machine learning (ML) has transformed manufacturing, particularly in optimizing production and quality control. A significant challenge in ML applications is obtaining sufficient training data, which data augmentation aims to address. While widely applied to image, text, and sound data, data augmentation for numerical data in manufacturing has seen limited investigation. This paper empirically compares three data augmentation techniques—generative adversarial networks, variational auto-encoders mixed with long-short-term memory, and warping—on four manufacturing datasets. It also provides a literature review, highlighting that generative models are the most common technique for numerical manufacturing data. Preliminary findings suggest that generative adversarial networks are effective for non-time-series numerical data, especially with datasets featuring many correlated model features, multiple machines, and sufficient instances and labels. This research enhances the understanding of data augmentation in manufacturing ML applications, emphasizing the need for tailored strategies.