Vol. 4 No. 2 (2024): African Journal of Artificial Intelligence and Sustainable Development
Articles

The Role of AI-Driven Predictive Maintenance in Enhancing U.S. Competitiveness and Efficiency in Aerospace Manufacturing

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  • Dr. Anna Schmidt
Dr. Anna Schmidt
Professor of Human-Computer Interaction, Swinburne University of Technology, Australia

Published 12-09-2024

Keywords

  • Predictive Maintenance,
  • Aerospace Manufacturing
Creative Commons License

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

How to Cite

[1]
Dr. Anna Schmidt, “The Role of AI-Driven Predictive Maintenance in Enhancing U.S. Competitiveness and Efficiency in Aerospace Manufacturing”, African J. of Artificial Int. and Sust. Dev., vol. 4, no. 2, pp. 281–296, Sep. 2024, Accessed: Dec. 22, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/165

Abstract

AI-driven predictive maintenance has the potential to significantly enhance the competitiveness and efficiency of U.S. aerospace manufacturing. Over the next five or ten years, it will go from being an “interesting idea” that people are beginning to explore to becoming ubiquitous in manufacturing and a key selling point for manufacturers. It encompasses combining AI-based intelligent edge systems and machine learning algorithm models for real-time predictive maintenance of production equipment. It can help manufacturers avoid unexpected breakdowns of critical production and experimental equipment on the factory floor. By monitoring critical equipment continuously, predictive maintenance can recognize new failures earlier, repair them before they become catastrophic, and reduce downtime. In turn, this will enable manufacturing resources to be utilized fully in producing and qualifying aerospace systems of the highest quality.

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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. Paul, Debasish, Gunaseelan Namperumal, and Yeswanth Surampudi. "Optimizing LLM Training for Financial Services: Best Practices for Model Accuracy, Risk Management, and Compliance in AI-Powered Financial Applications." Journal of Artificial Intelligence Research and Applications 3.2 (2023): 550-588.
  22. Namperumal, Gunaseelan, Akila Selvaraj, and Yeswanth Surampudi. "Synthetic Data Generation for Credit Scoring Models: Leveraging AI and Machine Learning to Improve Predictive Accuracy and Reduce Bias in Financial Services." Journal of Artificial Intelligence Research 2.1 (2022): 168-204.
  23. Soundarapandiyan, Rajalakshmi, Praveen Sivathapandi, and Yeswanth Surampudi. "Enhancing Algorithmic Trading Strategies with Synthetic Market Data: AI/ML Approaches for Simulating High-Frequency Trading Environments." Journal of Artificial Intelligence Research and Applications 2.1 (2022): 333-373.

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