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

Innovations and Applications

Authors

  • Dr. Vincent Wong Associate Professor of Computer Science, Hong Kong University of Science and Technology (HKUST) Author

Keywords:

Predictive Maintenance, Mobile Device Manufacturing

Abstract

The introduction of AI-driven predictive maintenance in U.S. mobile device manufacturing operations marks a significant shift in maintenance approaches, moving from corrective to predictive strategies. This shift is driven by the integration of modern technologies such as the Internet of Things (IoT) and RFID, which automate manual tasks and enable the acquisition, integration, and analysis of industrial data sources to support maintenance processes [1]. Predictive maintenance, empowered by Artificial Intelligence (AI) inference based on existing data, allows for fault detection, identification, and optimal maintenance strategy selection, while also addressing challenges related to data quality and equipment monitoring [2]. The application of Machine Learning (ML) methods in predictive maintenance has become a powerful tool for processing big data and uncovering hidden correlations, contributing to more efficient data collection and successful prediction for maintenance.

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Published

03-10-2024

How to Cite

[1]
Dr. Vincent Wong, “The Role of AI-Driven Predictive Maintenance in Enhancing U.S. Mobile Device Manufacturing Operations: Innovations and Applications”, Australian Journal of Machine Learning Research & Applications, vol. 4, no. 2, pp. 218–228, Oct. 2024, Accessed: Dec. 22, 2024. [Online]. Available: https://sydneyacademics.com/index.php/ajmlra/article/view/152

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