Artificial Intelligence (AI) Powered Detection and Identification of
Oil and Gas Pressure Vessel Damage Mechanisms
Masters of science (MSc) 2025
By
Mohamed Salah Mohamed Hewieg
Supervisors
Mechanical Engineering Department
College of Engineering and Technology
Arab Academy for Science and Technology and Maritime Transport
Mechanical Engineering Department
College of Engineering and Technology – Alamein
Arab Academy for Science and Technology and Maritime Transport
Abstract
Regular visual inspections of pressure vessels by qualified inspectors are vital in the oil and gas industry to ensure vessel integrity, prevent catastrophic failures, and avoid the consequences of misdiagnosis. In the present study, a deep learning (DL) model is proposed to perform visual inspection of pressure vessels using the You Only Look Once (YOLO) v8s model. Initially, binary classification is developed to diagnose whether the pressure vessel’s exterior shell is in excellent condition or includes damage using a training dataset of 5500 real shell surface on-site images from the Abu Madi gas field of the PETROBEL Company, Egypt. An additional model was trained on the same dataset of binary model for multi-class identification of external damages (i.e. corrosion, painting damage, mechanical damage, or brittle fracture). Three models were compared; namely YOLO v5s, YOLO v8s, and YOLO v9s. The highest performance model was YOLO v8s. Its detection accuracy reached 94.55% and 92.27% for binary classification and multi-class classification models, respectively.
Also, this study proposes a deep learning (DL) model as an intelligent and accurate tool for visual inspection of pressure vessels using a training dataset of 5000 real internal shell surface on-site images from the Abu Madi gas field of the PETROBEL Company, Egypt. A six-year-experienced Non-Destructive Testing (NDT) inspector was leveraged for manual labeling of the dataset. the present detection model utilizes You Only Look Once (YOLO) v8 model from v5, v8 and v10 models to diagnose whether the pressure vessel’s inner surfaces of shell is in good condition or include damage. The developed YOLO v8 multi-class identification model successfully detects the state of the pressure vessel’s inner shell; good condition, or in the existence of damage, corrosion, pitting corrosion, mechanical damage, or brittle fracture. After training the model on the used on-site dataset images, the test process reveals a detection accuracy up to 93.3%. During the test process, if a single image contains good and damaged parts or two different types of damage, the model can differentiate between those cases.
Implementing this solution in the inspection process will result in a cost reduction since it decreases the need for scaffolding and trained inspectors. This study provides a valuable roadmap for future research on image processing-based pressure vessel damage detection.
Eng. Hewieg MSc Thesis
Published Papers
Seddik, E., Hewieg, M., Afify, R., “AI-based Detection of Pressure Vessel Internal Damage Mechanisms“, 34th International Conference on Computer Theory and Applications (ICCTA 2024), Alex, Egypt, December 14-16, 2024. Certificate
