Vol. 3 No. 2 (2023): African Journal of Artificial Intelligence and Sustainable Development
Articles

Machine Learning-based Predictive Maintenance for Autonomous Vehicle Components

Dr. Opeoluwa Fawole
Associate Professor of Artificial Intelligence, University of Lagos (UNILAG)
Cover

Published 14-09-2023

How to Cite

[1]
Dr. Opeoluwa Fawole, “Machine Learning-based Predictive Maintenance for Autonomous Vehicle Components”, African J. of Artificial Int. and Sust. Dev., vol. 3, no. 2, pp. 266–291, Sep. 2023, Accessed: Nov. 07, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/87

Abstract

The aim of this paper is focused in developing a reliable, fault-tolerant and modular predictive maintenance approach for the Falcon II, a fully electric ground-based vehicle for autonomous applications [1]. Furthermore, to prove the performance of the project and to exploit the Falcon II public showcase potential, the diagnostic system will be implemented on-board reducing the constraint of a priori designed system and facing the real problem above exposed. In contrast to the classical development an on-board solution could bring accuracy improvements due to the availability of a larger informative context of the vehicle, including for instance, environmental conditions and mass in the vehicle, both factors strongly affecting the components performances.

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References

  1. Tatineni, S., and A. Katari. “Advanced AI-Driven Techniques for Integrating DevOps and MLOps: Enhancing Continuous Integration, Deployment, and Monitoring in Machine Learning Projects”. Journal of Science & Technology, vol. 2, no. 2, July 2021, pp. 68-98, https://thesciencebrigade.com/jst/article/view/243.
  2. Prabhod, Kummaragunta Joel. "Advanced Techniques in Reinforcement Learning and Deep Learning for Autonomous Vehicle Navigation: Integrating Large Language Models for Real-Time Decision Making." Journal of AI-Assisted Scientific Discovery 3.1 (2023): 1-20.
  3. Tatineni, Sumanth, and Sandeep Chinamanagonda. “Leveraging Artificial Intelligence for Predictive Analytics in DevOps: Enhancing Continuous Integration and Continuous Deployment Pipelines for Optimal Performance”. Journal of Artificial Intelligence Research and Applications, vol. 1, no. 1, Feb. 2021, pp. 103-38, https://aimlstudies.co.uk/index.php/jaira/article/view/104.