ABSTRACT
MACHINE LEARNING INNOVATIONS IN PREDICTIVE MAINTENANCE: A COMPREHENSIVE REVIEW OF APPLICATIONS IN THE MINING SECTOR
Journal: Earth Sciences Pakistan (ESP)
Author: Joachim Osheyor Gidiagba, Blessed Afeyokalo Egbokhaebho, Oluwaseun Ayo Ogunjobi, Kelechi Anthony Ofonagoro, Chibuike Daraojimba
This is an open access article distributed under the Creative Commons Attribution License CC BY 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
Doi: 10.26480/esp.02.2023.48.56
In recent years, machine learning (ML) has burgeoned as a transformative tool, particularly within predictive maintenance applications. The mining sector, characterized by its heavy machinery and capital-intensive equipment, stands to benefit immensely from advancements in predictive maintenance techniques. This comprehensive review delves into the recent innovations in ML-driven predictive maintenance and their significant applications within the mining industry. Drawing from an array of case studies and empirical analyses, this paper underscores the tangible operational efficiencies and cost-saving benefits brought about by these ML methodologies. Furthermore, it offers critical insights into the challenges, best practices, and the potential future trajectory of this intersection of machine learning and mining operations.
| Pages | 48-56 |
| Year | 2023 |
| Issue | 2 |
| Volume | 7 |
