TY - GEN
T1 - Harnessing the Fourth Industrial Revolution
T2 - 11th International Conference on Robot Intelligence Technology and Applications, RiTA 2023
AU - Alqaraleh, Doaa Ahmad
AU - Hajjaj, Sami
AU - Mohamed, Hassan
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
KW - Industry 4.0
KW - manufacturing industry
KW - Mining Sector
KW - Predictive Maintenance
UR - http://www.scopus.com/inward/record.url?scp=85210883720&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-70684-4_5
DO - 10.1007/978-3-031-70684-4_5
M3 - Conference Proceeding
AN - SCOPUS:85210883720
SN - 9783031706837
T3 - Lecture Notes in Networks and Systems
SP - 53
EP - 74
BT - Robot Intelligence Technology and Applications 8 - Results from the 11th International Conference on Robot Intelligence Technology and Applications
A2 - Abdul Majeed, Anwar P.P.
A2 - Yap, Eng Hwa
A2 - Liu, Pengcheng
A2 - Huang, Xiaowei
A2 - Nguyen, Anh
A2 - Chen, Wei
A2 - Kim, Ue-Hwan
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 6 December 2023 through 8 December 2023
ER -
