Recent advancement in industrial artificial intelligence (AI) is reshaping the industry, driving smarter manufacturing, predictive maintenance, and intelligent decision-making. However, existing approaches often focus primarily on algorithms and models, overlooking the importance of systematically integrating domain knowledge, data, and models to ensure more comprehensive and effective AI solutions. Therefore, the effective development and deployment of Industrial AI solutions require a more comprehensive and systematic approach. To address this gap, this paper summarizes previous research and rethinks the role of industrial AI and presents a unified industrial AI foundation framework comprising three core modules: knowledge module, data module, and model module. These modules help to extend and enhance the industrial AI methodology platform, supporting various industrial applications. In addition, a case study on rotating machinery diagnosis demonstrates the framework's effectiveness, and several future directions are highlighted for the development of the industrial AI foundation framework.

翻译：工业人工智能（AI）的最新进展正在重塑工业格局，推动智能制造、预测性维护和智能决策的发展。然而，现有方法往往主要关注算法和模型，忽视了系统性地整合领域知识、数据与模型以确保更全面、更有效的AI解决方案的重要性。因此，工业AI解决方案的有效开发与部署需要一个更全面、更系统化的方法。为弥补这一不足，本文总结了先前研究，重新思考了工业AI的角色，并提出了一个统一的工业AI基础框架，该框架包含三个核心模块：知识模块、数据模块和模型模块。这些模块有助于扩展和增强工业AI方法论平台，支持各种工业应用。此外，通过一个旋转机械诊断的案例研究展示了该框架的有效性，并指出了工业AI基础框架未来发展的若干方向。