A Survey on Foundation Models for Industrial Intelligence: Architectures, Training Paradigms, and Applications

The rapid digital transformation of modern industrial systems has led to the generation of massive volumes of heterogeneous data from sensors, industrial machines, manufacturing platforms, and enterprise information systems. Traditional machine learning approaches often struggle to effectively exploit such large-scale and complex datasets. Recent advances in foundation models, including large-scale transformer-based architectures, have demonstrated remarkable capabilities in learning generalizable representations from massive multimodal data. These models provide new opportunities for developing intelligent and scalable solutions in Industry 4.0 environments. This paper presents a comprehensive survey of foundation models for industrial intelligence. We review the evolution of artificial intelligence architectures from conventional machine learning to large-scale foundation models and analyze key architectural components that enable scalable industrial AI systems. In addition, we examine emerging training paradigms, including large-scale data curation, self-supervised learning, and distributed training infrastructures designed for industrial datasets. The survey further highlights major industrial applications such as predictive maintenance, automated quality inspection, and intelligent manufacturing systems. Finally, we discuss key challenges related to data availability, domain adaptation, realtime constraints, safety, and energy consumption, and outline promising research directions for future industrial AI systems.