Publication: Harnessing the Fourth Industrial Revolution: Trends and Challenges in Applying Industry 4.0 to Enhance Predictive Maintenance in Manufacturing, Specifically Mining
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Date
2024
Authors
Alqaraleh D.A.
Hajjaj S.
Mohamed H.
Journal Title
Journal ISSN
Volume Title
Publisher
Springer Science and Business Media Deutschland GmbH
Abstract
The combination of advanced technologies and data-driven decision-making in the Fourth Industrial Revolution presents substantial opportunities for improving manufacturing processes, particularly in the realm of predictive maintenance. Nevertheless, the utilization of this technology in the mining industry has not been thoroughly investigated. This research investigates the effects of Industry 4.0 on the practice of predictive maintenance within the manufacturing sector, with a specific emphasis on the mining industry. This study examines the various patterns and challenges associated with the implementation process, thereby addressing a gap in the existing literature. This research investigates the potential of Industry 4.0 in enhancing predictive maintenance within the manufacturing sector, with a specific focus on the mining industry. The study utilizes a content-centric methodology to examine the field of sustainable manufacturing, with a particular emphasis on identifying and evaluating promising technologies such as cyber-physical systems, the Internet of Things (IoT), big data analytics, digital twins, augmented reality (AR), and artificial intelligence (AI). Nevertheless, it is imperative to acknowledge and tackle various challenges that arise during the deployment of technology, such as security concerns, human factors, and the need for procedural enhancements. ? The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
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Keywords
Industry 4.0 , Mining industry , Smart manufacturing , Advanced technology , Data driven decision , Decisions makings , Implementation process , Industrial revolutions , Manufacturing industries , Manufacturing process , Manufacturing sector , Mining sector , Predictive maintenance , Predictive maintenance