Towards automated remote inspection of anomalies in offshore components.

Toral Quijas, Luis Alberto

Abstract

This dissertation marks a significant advancement in offshore structural inspections, focusing on the development, integration and evaluation of advanced deep-learning models. The research encompasses: a thorough literature review, identifying innovation opportunities in deep learning for industrial inspections; the development of a general classification model using cutting-edge architectures for precise classification of circumferential welds; the design and training of an anomaly detection model to enhance fault identification; the implementation of a human-in-the-loop system for improved model accuracy and reliability; and a comprehensive evaluation of these models' real-world applicability. The study not only showcases cutting-edge deep learning techniques for defect detection, but also highlights critical research gaps, providing a guide for future investigation. The novel incorporation of human expertise with machine learning via a human-in-the-loop approach is a significant innovation, bolstering decision-making and potentially lowering error rates. This research presents a comprehensive model that could serve as a benchmark in the field, valuable to both academics and industry professionals. It concludes by reflecting on the framework's successes and limitations, discussing its implications for offshore inspection practices, and suggesting future research directions and potential broader industry impacts.

Citation

TORAL QUIJAS, L.A. 2024. Towards automated remote inspection of anomalies in offshore components. Robert Gordon University, MRes thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-2801306

• Thesis Type: Thesis
• Deposit Date: Apr 23, 2025
• Publicly Available Date: Apr 23, 2025
• DOI: https://doi.org/10.48526/rgu-wt-2801306
• Keywords: Deep learning; Machine learning; Remote sensing; Fault detection; Offshore engineering
• Public URL: https://rgu-repository.worktribe.com/output/2801306
• Award Date: Jul 31, 2024