Rethinking industrial artificial intelligence: a unified foundation framework

• URL: http://arxiv.org/abs/2504.01797v2
• Date: Thu, 17 Apr 2025 02:54:57 GMT
• Title: Rethinking industrial artificial intelligence: a unified foundation framework
• Authors: Jay Lee, Hanqi Su,
• Abstract summary: Recent advancements in industrial artificial intelligence (AI) are reshaping the industry by driving smarter manufacturing, predictive maintenance, and intelligent decision-making.<n>Existing approaches often focus primarily on algorithms and models while overlooking the importance of systematically integrating domain knowledge, data, and models.<n>This paper reviews previous research, rethinks the role of industrial AI, and proposes a unified industrial AI foundation framework.
• Score: 0.32885740436059047
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Engineering Artificial Intelligence: Framework, Challenges, and Future Direction [0.2678472239880052]
  This paper introduces the "ABCDE" as the key elements of Engineering AI. It proposes a unified, systematic engineering AI ecosystem framework. By providing a comprehensive perspective, this paper aims to advance the strategic implementation of AI.
  arXiv  Detail & Related papers  (2025-04-03T04:30:10Z)
• Collaborative AI in Sentiment Analysis: System Architecture, Data Prediction and Deployment Strategies [3.3374611485861116]
  Large language model (LLM) based artificial intelligence technologies have been a game-changer, particularly in sentiment analysis. However, integrating diverse AI models for processing complex multimodal data and the associated high costs of feature extraction presents significant challenges. This study introduces a collaborative AI framework designed to efficiently distribute and resolve tasks across various AI systems.
  arXiv  Detail & Related papers  (2024-10-17T06:14:34Z)
• Generative AI Application for Building Industry [10.154329382433213]
  This paper investigates the transformative potential of generative AI technologies, particularly large language models (LLMs) in the building industry. The research highlights how LLMs can automate labor-intensive processes, significantly improving efficiency, accuracy, and safety in building practices.
  arXiv  Detail & Related papers  (2024-10-01T21:59:08Z)
• Next-Gen Software Engineering. Big Models for AI-Augmented Model-Driven Software Engineering [0.0]
  The paper provides an overview of the current state of AI-augmented software engineering and develops a corresponding taxonomy, AI4SE.<n>A vision of AI-assisted Big Models in SE is put forth, with the aim of capitalising on the advantages inherent to both approaches in the context of software development.
  arXiv  Detail & Related papers  (2024-09-26T16:49:57Z)
• Sustainable Diffusion-based Incentive Mechanism for Generative AI-driven Digital Twins in Industrial Cyber-Physical Systems [65.22300383287904]
  Industrial Cyber-Physical Systems (ICPSs) are an integral component of modern manufacturing and industries.<n>By digitizing data throughout product life cycles, Digital Twins (DTs) in ICPSs enable a shift from current industrial infrastructures to intelligent and adaptive infrastructures.<n>GenAI can drive the construction and update of DTs to improve predictive accuracy and prepare for diverse smart manufacturing.
  arXiv  Detail & Related papers  (2024-08-02T10:47:10Z)
• Position Paper: Agent AI Towards a Holistic Intelligence [53.35971598180146]
  We emphasize developing Agent AI -- an embodied system that integrates large foundation models into agent actions. In this paper, we propose a novel large action model to achieve embodied intelligent behavior, the Agent Foundation Model.
  arXiv  Detail & Related papers  (2024-02-28T16:09:56Z)
• Machine Learning Insides OptVerse AI Solver: Design Principles and Applications [74.67495900436728]
  We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
  arXiv  Detail & Related papers  (2024-01-11T15:02:15Z)
• Artificial Intelligence for IT Operations (AIOPS) Workshop White Paper [50.25428141435537]
  Artificial Intelligence for IT Operations (AIOps) is an emerging interdisciplinary field arising in the intersection between machine learning, big data, streaming analytics, and the management of IT operations. Main aim of the AIOPS workshop is to bring together researchers from both academia and industry to present their experiences, results, and work in progress in this field.
  arXiv  Detail & Related papers  (2021-01-15T10:43:10Z)
• Validate and Enable Machine Learning in Industrial AI [47.20869253934116]
  Industrial AI promises more efficient future industrial control systems. The Petuum Optimum system is used as an example to showcase the challenges in making and testing AI models.
  arXiv  Detail & Related papers  (2020-10-30T20:33:05Z)
• AI-based Modeling and Data-driven Evaluation for Smart Manufacturing Processes [56.65379135797867]
  We propose a dynamic algorithm for gaining useful insights about semiconductor manufacturing processes. We elaborate on the utilization of a Genetic Algorithm and Neural Network to propose an intelligent feature selection algorithm.
  arXiv  Detail & Related papers  (2020-08-29T14:57:53Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.