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

Deep Learning Algorithms for Predictive Maintenance in U.S. Supply Chain Operations: Enhancing Reliability and Efficiency

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  • Dr. Lisa Thompson
Dr. Lisa Thompson
Associate Professor of Computer Science, Stanford University, Palo Alto, USA

Published 18-09-2024

Keywords

  • Predictive Maintenance
Creative Commons License

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

How to Cite

[1]
Dr. Lisa Thompson, “Deep Learning Algorithms for Predictive Maintenance in U.S. Supply Chain Operations: Enhancing Reliability and Efficiency”, African J. of Artificial Int. and Sust. Dev., vol. 4, no. 2, pp. 102–123, Sep. 2024, Accessed: Nov. 21, 2024. [Online]. Available: https://africansciencegroup.com/index.php/AJAISD/article/view/155

Abstract

The introduction section serves as a foundational component of the essay, providing an overview of the study on deep learning algorithms for predictive maintenance in U.S. supply chain operations. It sets the stage for subsequent sections by outlining the context and significance of the research. In this context, [1] introduced a hybrid deep learning-based approach for disruption detection within a data-driven cognitive digital supply chain twin framework. Their approach enhances supply chain resilience by enabling real-time disruption detection, disrupted echelon identification, and time-to-recovery prediction. The framework combines a deep autoencoder neural network with a one-class support vector machine classification algorithm for disruption detection, and long-short term memory neural network models for disrupted echelon identification and time-to-recovery prediction. Furthermore, [2] plan to develop data preprocessing and compression techniques to reduce data transmission in the edge computing structure for real-time predictive maintenance, aiming to build a more efficient distributed edge computing system.

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