AIGC for Industrial Time Series: From Deep-Generative Models to Large-Generative Models
Lei Ren, Haiteng Wang, Jinwang Li, Yang Shan Tang, Chunhua Yang · IEEE Transactions on Systems Man and Cybernetics Systems · 2025
With the remarkable success of generative models like ChatGPT, artificial intelligence generated content (AIGC) is undergoing explosive development. Not limited to text and images, generative models can generate industrial time series data, addressing challenges, such as the difficulty of data collection and data annotation. Due to their outstanding generation ability, they have been widely used in Internet of Things, metaverse, and CPSS to enhance the efficiency of industrial production. In this article, we present a comprehensive overview of generative models for industrial time series from deep-generative models (DGMs) to large-generative models (LGMs). First, a DGM-based AIGC framework is proposed for industrial time series generation. Within this framework, we survey advanced industrial DGMs and present a multiperspective categorization. Then, we systematically propose the roadmap to construct industrial LGMs from four aspects: large-scale industrial dataset, LGMs architecture for complex industrial characteristics, self-supervised training for industrial time series, and fine-tuning of industrial downstream tasks. Furthermore, we introduce an evaluation benchmark that systematically assesses fidelity, diversity, and utility. We include a case study on aircraft engine maintenance, demonstrating the application of DGMs in industrial predictive maintenance. Finally, we conclude the challenges and future directions to enable the development of generative models in industry.