AIGC for Industrial Time Series: From Deep Generative Models to Large Generative Models

2024-07-16

arXiv.org

AI Summary

Industrial time-series data scarcity and high annotation costs hinder effective modeling and simulation. Method: This paper establishes a comprehensive industrial time-series AIGC framework spanning deep generative models (DGMs) to large generative models (LGMs). It introduces the first unified DGM framework with a multidimensional taxonomy; systematically defines LGMs’ four pillars—large-scale industrial time-series datasets, architecture designs adaptive to temporal dynamics, self-supervised pretraining (via masked reconstruction and contrastive learning), and domain-specific fine-tuning mechanisms (prompt engineering and adapter injection); and integrates VAEs, GANs, diffusion models, and multi-source heterogeneous data governance techniques. Contribution/Results: We present the first holistic roadmap from DGMs to LGMs for industrial time-series generation; identify critical deployment pathways for LGMs; and establish core evaluation dimensions—including interpretability and low annotation dependency. The framework delivers a scalable data-generation infrastructure for IoT, digital twins, and cyber-physical-social systems.

Technology Category

• Machine Learning: Deep Generative Models & Autoencoders
• Natural Language Processing: Generation
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

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 cyber-physical-social systems to enhance the efficiency of industrial production. In this paper, 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 multi-perspective categorization. Furthermore, we systematically analyze the critical technologies required 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. Finally, we conclude the challenges and future directions to enable the development of generative models in industry.

Problem

• Generating industrial time series data
• Addressing data collection challenges
• Enhancing industrial production efficiency

Innovation

• Deep Generative Models
• Large Generative Models
• Self-supervised Training