提出统一工业AI框架，整合知识、数据与模型。
Rethinking industrial artificial intelligence: a unified foundation framework
- 构建知识、数据、模型三模块统一框架
- 案例验证在旋转机械诊断中有效提升性能
- 适合工业界开发者与研究者参考应用
工业人工智能的最新进展正推动智能制造、预测性维护和智能决策的发展。然而，现有方法多聚焦算法与模型，忽视了领域知识、数据与模型的系统性融合，难以构建全面有效的工业AI解决方案。为此，本文回顾前期研究，重新思考工业AI的角色，提出一个包含知识模块、数据模块和模型模块的统一工业AI基础框架，旨在扩展并增强工业AI方法平台，支持多种工业应用场景。通过旋转机械故障诊断的案例研究，验证了该框架的有效性，并指出了未来发展方向。
原文摘要 · Abstract (English)
Recent advancements in industrial artificial intelligence (AI) are reshaping the industry by driving smarter manufacturing, predictive maintenance, and intelligent decision-making. However, existing approaches often focus primarily on algorithms and models while overlooking the importance of systematically integrating domain knowledge, data, and models to develop more comprehensive and effective AI solutions. Therefore, the effective development and deployment of industrial AI require a more comprehensive and systematic approach. To address this gap, this paper reviews previous research, rethinks the role of industrial AI, and proposes a unified industrial AI foundation framework comprising three core modules: the 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 is presented to demonstrate the effectiveness of the proposed framework, and several future directions are highlighted for the development of the industrial AI foundation framework.
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