Integrating Machine Learning into Digital Twin Frameworks for Predictive Surveillance and Automated Recovery of Deepwater Subsea Assets

• Author(s): Malvern Iheanyichukwu Odum ; Iduate Digitemie Jason ; Dazok Donald Jambol
• Paper ID: 1709568
• Page: 371-382
• Published Date: 31-05-2021
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 3 Issue 9 March-2020

Abstract

The increasing complexity and inaccessibility of deepwater subsea environments demand advanced, intelligent solutions for asset monitoring, fault prediction, and recovery. This paper explores the integration of machine learning into digital twin frameworks as a transformative approach for predictive surveillance and automated recovery of subsea infrastructure. By combining real-time data acquisition with intelligent algorithms, digital twins evolve from passive representations into proactive, decision-making systems capable of early anomaly detection, failure trajectory modeling, and autonomous intervention. The study examines key components of this integration, including data preprocessing, feature engineering, online model updating, and reinforcement learning-based decision support systems. It also discusses the development of cyber-physical feedback loops that enable actuation through remotely operated or autonomous vehicles in response to model-driven insights. The integration enhances adaptability, operational continuity, and system resilience, significantly reducing downtime and improving safety in remote offshore operations. This work underscores the potential of machine learning to redefine the role of digital twins in subsea engineering, paving the way for more autonomous, intelligent, and cost-effective asset management in extreme underwater conditions.

Keywords

Digital Twin, Machine Learning, Predictive Surveillance, Subsea Asset Management, Autonomous Recovery, Reinforcement Learning

Citations

IRE Journals:
Malvern Iheanyichukwu Odum , Iduate Digitemie Jason , Dazok Donald Jambol "Integrating Machine Learning into Digital Twin Frameworks for Predictive Surveillance and Automated Recovery of Deepwater Subsea Assets" Iconic Research And Engineering Journals Volume 3 Issue 9 2020 Page 371-382

IEEE:
Malvern Iheanyichukwu Odum , Iduate Digitemie Jason , Dazok Donald Jambol "Integrating Machine Learning into Digital Twin Frameworks for Predictive Surveillance and Automated Recovery of Deepwater Subsea Assets" Iconic Research And Engineering Journals, 3(9)