AI-driven predictive maintenance systems for loss prevention and asset protection in subsea operations

Sobiyi, Taiwo Oluwole and Egbuna, Christopher Chuks and Kareem, Saheed Remi and Lawal, Raphael Oluwatobiloba (2025) AI-driven predictive maintenance systems for loss prevention and asset protection in subsea operations. World Journal of Advanced Research and Reviews, 25 (2). pp. 923-933. ISSN 2581-9615

This comprehensive review examines the transformative role of artificial intelligence in revolutionizing predictive maintenance systems for subsea operations. Through systematic analysis of industry implementations, technological frameworks, and documented case studies, we investigate how AI-driven systems enhance asset protection and prevent losses in challenging underwater environments. Our research methodology encompasses qualitative and quantitative analysis of implementation data, focusing on system performance, operational benefits, and implementation challenges. The research reveals that AI-driven systems substantially improve equipment reliability and operational efficiency in subsea operations through enhanced prediction capabilities and optimized maintenance scheduling. We address critical challenges in sensor reliability, data transmission, and system integration, providing insights into effective implementation strategies and risk management approaches. The study presents a framework for AI integration in subsea maintenance that considers both technical requirements and organizational factors, incorporating emerging trends in deep learning, digital twin technology, and real-time monitoring systems. This work contributes to the growing body of literature on digital transformation in subsea operations by offering a comprehensive analysis of AI's role in creating more efficient, reliable, and cost-effective maintenance systems.