THE RISE OF SMART ASSET MANAGEMENT: A REVIEW OF ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING APPLICATIONS IN ENERGY FACILITIES MAINTENANCE
Joachim Osheyor Gidiagba, Joel Leonard, 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
Artificial Intelligence (AI) and Machine Learning (ML) have emerged as transformative tools in the realm of energy facilities maintenance. This comprehensive review delves deep into the multifaceted applications, challenges, and practical implementations of AI and ML technologies in this domain. Central to our discussion is an exploration of AI/ML-driven strategies such as predictive maintenance, condition-based monitoring, operational optimization, anomaly detection, advanced robotics, and enhancement of energy efficiency. While these tools herald a paradigm shift in maintenance practices, several challenges including data integrity, seamless integration, privacy implications, and ethical considerations remain. The study peer into the future to anticipate further refinements in AI/ML algorithms, tighter integration with burgeoning technological frontiers, augmented scalability, and a heightened focus on the implications for energy sustainability. This review aspires to serve as a critical reference point for scholars and practitioners alike, illuminating pathways for more efficient and sustainable operations of energy facilities.
Smart Asset Management, Artificial Intelligence, Facility, Maintenance, Machine Learning, Robotics.