Advancing Industrial Data Augmentation in AIGC Era: From Foundations to Frontier Applications

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.