Vol. 4 No. 2 (2024): Journal of AI-Assisted Scientific Discovery
Articles

The Role of AI-Driven Predictive Maintenance in Enhancing U.S. Manufacturing Operations

PDF
  • Dr. Yu Han
Dr. Yu Han
Associate Professor of Computer Science, Shanghai Jiao Tong University, China
Cover

Published 30-09-2024

Keywords

  • Predictive Maintenance,
  • Manufacturing Operations
Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

How to Cite

[1]
Dr. Yu Han, “The Role of AI-Driven Predictive Maintenance in Enhancing U.S. Manufacturing Operations”, Journal of AI-Assisted Scientific Discovery, vol. 4, no. 2, pp. 246–266, Sep. 2024, Accessed: Nov. 14, 2024. [Online]. Available: https://scienceacadpress.com/index.php/jaasd/article/view/160

Abstract

Manufacturing is at the core of the U.S. economy. The long-term vitality of the manufacturing sector has a direct relationship with the vitality of the overall national economy. Rapid revitalization and improvement in the global competitiveness of the U.S. manufacturing sector are essential for sustainable economic recovery and growth. In recent years, the manufacturing industry has undergone dramatic changes due to rising competitive pressure, deregulation and offshoring, and advancing technology. The increasing capability of machine learning, artificial intelligence (AI), and automation technologies combined with the greater availability of machine data have created opportunities for smart and data-driven manufacturing systems.

PDF

Downloads

Download data is not yet available.

References

  1. Sengottaiyan, Krishnamoorthy, and Manojdeep Singh Jasrotia. "Relocation of Manufacturing Lines-A Structured Approach for Success." International Journal of Science and Research (IJSR) 13.6 (2024): 1176-1181.
  2. Gayam, Swaroop Reddy. "Artificial Intelligence for Natural Language Processing: Techniques for Sentiment Analysis, Language Translation, and Conversational Agents." Journal of Artificial Intelligence Research and Applications 1.1 (2021): 175-216.
  3. Nimmagadda, Venkata Siva Prakash. "Artificial Intelligence for Compliance and Regulatory Reporting in Banking: Advanced Techniques, Models, and Real-World Applications." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 151-189.
  4. Putha, Sudharshan. "AI-Driven Natural Language Processing for Voice-Activated Vehicle Control and Infotainment Systems." Journal of Artificial Intelligence Research and Applications 2.1 (2022): 255-295.
  5. Sahu, Mohit Kumar. "Machine Learning Algorithms for Personalized Financial Services and Customer Engagement: Techniques, Models, and Real-World Case Studies." Distributed Learning and Broad Applications in Scientific Research 6 (2020): 272-313.
  6. Kasaraneni, Bhavani Prasad. "Advanced Machine Learning Models for Risk-Based Pricing in Health Insurance: Techniques and Applications." Australian Journal of Machine Learning Research & Applications 1.1 (2021): 170-207.
  7. Kondapaka, Krishna Kanth. "Advanced Artificial Intelligence Models for Predictive Analytics in Insurance: Techniques, Applications, and Real-World Case Studies." Australian Journal of Machine Learning Research & Applications 1.1 (2021): 244-290.
  8. Kasaraneni, Ramana Kumar. "AI-Enhanced Pharmacoeconomics: Evaluating Cost-Effectiveness and Budget Impact of New Pharmaceuticals." Australian Journal of Machine Learning Research & Applications 1.1 (2021): 291-327.
  9. Pattyam, Sandeep Pushyamitra. "AI-Driven Data Science for Environmental Monitoring: Techniques for Data Collection, Analysis, and Predictive Modeling." Australian Journal of Machine Learning Research & Applications 1.1 (2021): 132-169.
  10. Kuna, Siva Sarana. "Reinforcement Learning for Optimizing Insurance Portfolio Management." African Journal of Artificial Intelligence and Sustainable Development 2.2 (2022): 289-334.
  11. Gayam, Swaroop Reddy, Ramswaroop Reddy Yellu, and Praveen Thuniki. "Artificial Intelligence for Real-Time Predictive Analytics: Advanced Algorithms and Applications in Dynamic Data Environments." Distributed Learning and Broad Applications in Scientific Research 7 (2021): 18-37.
  12. Nimmagadda, Venkata Siva Prakash. "Artificial Intelligence for Customer Behavior Analysis in Insurance: Advanced Models, Techniques, and Real-World Applications." Journal of AI in Healthcare and Medicine 2.1 (2022): 227-263.
  13. Putha, Sudharshan. "AI-Driven Personalization in E-Commerce: Enhancing Customer Experience and Sales through Advanced Data Analytics." Journal of Bioinformatics and Artificial Intelligence 1.1 (2021): 225-271.
  14. Sahu, Mohit Kumar. "Machine Learning for Personalized Insurance Products: Advanced Techniques, Models, and Real-World Applications." African Journal of Artificial Intelligence and Sustainable Development 1.1 (2021): 60-99.
  15. Kasaraneni, Bhavani Prasad. "AI-Driven Approaches for Fraud Prevention in Health Insurance: Techniques, Models, and Case Studies." African Journal of Artificial Intelligence and Sustainable Development 1.1 (2021): 136-180.
  16. Kondapaka, Krishna Kanth. "Advanced Artificial Intelligence Techniques for Demand Forecasting in Retail Supply Chains: Models, Applications, and Real-World Case Studies." African Journal of Artificial Intelligence and Sustainable Development 1.1 (2021): 180-218.
  17. Kasaraneni, Ramana Kumar. "AI-Enhanced Portfolio Optimization: Balancing Risk and Return with Machine Learning Models." African Journal of Artificial Intelligence and Sustainable Development 1.1 (2021): 219-265.
  18. Pattyam, Sandeep Pushyamitra. "AI-Driven Financial Market Analysis: Advanced Techniques for Stock Price Prediction, Risk Management, and Automated Trading." African Journal of Artificial Intelligence and Sustainable Development 1.1 (2021): 100-135.
  19. Kuna, Siva Sarana. "The Impact of AI on Actuarial Science in the Insurance Industry." Journal of Artificial Intelligence Research and Applications 2.2 (2022): 451-493.
  20. Nimmagadda, Venkata Siva Prakash. "Artificial Intelligence for Dynamic Pricing in Insurance: Advanced Techniques, Models, and Real-World Application." Hong Kong Journal of AI and Medicine 4.1 (2024): 258-297.
  21. Selvaraj, Akila, Praveen Sivathapandi, and Rajalakshmi Soundarapandiyan. "Blockchain-Based Cybersecurity Solutions for Automotive Industry: Protecting Over-the-Air (OTA) Software Updates in Autonomous and Connected Vehicles." Cybersecurity and Network Defense Research 3.2 (2023): 86-134.
  22. Paul, Debasish, Gunaseelan Namperumal, and Akila Selvaraj. "Cloud-Native AI/ML Pipelines: Best Practices for Continuous Integration, Deployment, and Monitoring in Enterprise Applications." Journal of Artificial Intelligence Research 2.1 (2022): 176-231.
  23. Namperumal, Gunaseelan, Sharmila Ramasundaram Sudharsanam, and Rajalakshmi Soundarapandiyan. "Data-Driven Workforce Management in Cloud HCM Solutions: Utilizing Big Data and Analytics for Strategic Human Resources Planning." Australian Journal of Machine Learning Research & Applications 2.2 (2022): 549-591.
  24. Soundarapandiyan, Rajalakshmi, Yeswanth Surampudi, and Akila Selvaraj. "Intrusion Detection Systems for Automotive Networks: Implementing AI-Powered Solutions to Enhance Cybersecurity in In-Vehicle Communication Protocols." Cybersecurity and Network Defense Research 3.2 (2023): 41-86.
  25. Sudharsanam, Sharmila Ramasundaram, Praveen Sivathapandi, and Yeswanth Surampudi. "Cloud-Based Telematics and Real-Time Data Integration for Fleet Management: A Comprehensive Analysis of IoT-Driven Predictive Analytics Models." Journal of Artificial Intelligence Research and Applications 3.1 (2023): 622-657.

Similar Articles

You may also start an advanced similarity search for this article.