A Digital Twin Framework for Corrosion Predictive Analysis

Noor Ul Eeman; Wasim Ahmad; Mirza Jahanzaib; Salman Hussain

doi:10.58915/aset.v5i1.3202

Authors

Noor Ul Eeman University of Engineering and Technology, Pakistan
Wasim Ahmad University of Engineering and Technology, Pakistan
Mirza Jahanzaib University of Engineering and Technology, Pakistan
Salman Hussain University of Engineering and Technology, Pakistan

DOI:

https://doi.org/10.58915/aset.v5i1.3202

Keywords:

Corrosion, Data Visualization Dashboard, Digital Twin framework, Predictive Corrosion Analysis, YOLOv11

Abstract

In today’s dynamic environment, digital twins are gaining attraction for predictive maintenance. Existing research lacks the solutions that integrate advanced computer vision with intuitive visualization for corrosion analysis. This study aims to develop a Digital Twin framework for predictive corrosion analysis to improve asset integrity and operational performance from an Industry 4.0 perspective. The proposed framework focuses on offshore and onshore oil and gas platforms. In order to automatically identify the corrosion, YOLOv8 and YOLOv11 object detection models have been used to pre-process and analyze a curated corrosion dataset. The findings of the study reveal that with 93.6% accuracy and an F1-score of 0.92, YOLOv11 performs better than YOLOv8 (88.1% accuracy and 0.71 F1-score). It demonstrates the robustness of the YOLOv11 in diverse platform environments. Further, in the proposed Digital Twin framework, a corrosion data visualization dashboard has been designed to convert detection outputs into practical insights. The dashboard comprises corrosion severity indicators, spatial mapping, and performance metrics, enabling support for Reliability-Centered Maintenance (RCM) and Risk-Based Inspection (RBI). The study contributes to knowledge by benchmarking state-of-the-art detection models and presenting a corrosion-focused Digital Twin framework that integrates AI-enabled sensing with decision-support visualization.

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