Advancing Industrial Data Augmentation in AIGC Era: From Foundations to Frontier Applications
Xiaoyu Jiang, Chen Zheng, Yue Zhuo, Xiangyin Kong, Zhiqiang Ge, Zhihuan Song, Min Xie
Abstract
In the field of intelligent manufacturing and industrial big data, data-driven Industrial Intelligence Models (IIMs) based on machine learning have become indispensable for modern industrial systems. While IIMs are renowned for their effective learning capabilities, a critical challenge persists: Their performance is severely compromised by the substantial quality gap between raw industrial data and model-ready data. To address this core issue, Industrial Data Augmentation (IDA) has emerged as a transformative solution, yet existing research lacks systematic frameworks and implementation guidelines. This paper presents the first comprehensive survey establishing IDA as an independent research domain. We propose a novel taxonomy categorizing IDA methods by transformation-based, interpolation-based, and distribution estimation-based approaches. Beyond methodology analysis, we conduct a systematic review of frontier IDA applications spanning key performance indicator prediction, anomaly monitoring, fault diagnosis, and defect detection. Significantly, an open-source IDA toolbox implementing twenty IDA algorithms is introduced to facilitate ongoing development and application, available at https://github.com/3uchen/IdaLy. Finally, this paper highlights current challenges and future prospects for the IDA, seeking to motivate and steer further research in this area.