SECURING LNG TERMINALS RELIABILITY: A COMPREHENSIVE LITERATURE REVIEW OF RELIABILITY-CENTERED MAINTENANCE STRATEGIES
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
The reliable operation of Liquified Natural Gas (LNG) terminals is of paramount importance for the energy industry. This paper presents a comprehensive literature review of Reliability-Centered Maintenance (RCM) strategies tailored to secure the reliability of LNG terminals. The study explores the unique challenges faced by LNG terminals, ranging from equipment complexity to safety regulations. It examines the principles and components of RCM processes, highlighting the benefits and limitations. Additionally, the paper delves into the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) applications in enhancing LNG terminal maintenance. Real-world case studies demonstrate the practical implementation of AI/ML-driven predictive maintenance, robotic inspection, condition monitoring, and anomaly detection. The challenges and limitations associated with the adoption of AI/ML in LNG terminal maintenance are discussed, along with future opportunities for innovation and integration with emerging technologies. The study concludes by emphasizing the significance of embracing AI/ML for enhancing LNG terminal reliability and sustainability in the evolving energy landscape.