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Integrating Machine Learning into Digital Twin Frameworks for Predictive Surveillance and Automated Recovery of Deepwater Subsea Assets
Subject area: Science,Engineering and Technology · Area of research: Machine Learning
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
How to cite this paper
@article{1709568,
author = {Malvern Iheanyichukwu Odum, Iduate Digitemie Jason, Dazok Donald Jambol},
title = {Integrating Machine Learning into Digital Twin Frameworks for Predictive Surveillance and Automated Recovery of Deepwater Subsea Assets},
journal = {Iconic Research And Engineering Journals},
year = {2020},
volume = {3},
number = {9},
pages = {371-382},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1709568.pdf},
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},
month = {March},
}