With minimum breaking loads (MBL) equivalent to steel wire at The application of fibre ropes in offshore lifting operations has significant potential forįurther development. It also proposes sustainable asset management approaches as guidelines that are advised, with policy implications. In this study, the management of offshore structures were also presented with some discussions on fault monitoring using sensors. This paper presents the guidelines on asset monitoring, sustainable maintenance, and safety practices for offshore structures. Due to the limited space and remote location of most offshore operations, producing cost-effective, efficient, and long-lasting equipment necessitates a high level of competence. The ocean environment is constantly corrosive, and the production activities demand extremely high levels of safety and reliability. This paper also discusses fault diagnosis using sensors in the offshore facilities. Maintaining existing assets in the field and developing new platforms that are capable of extracting future oil and gas resources are the two key issues facing the offshore sector. The design and construction of offshore structures require some materials that are used to make the structural units, such as offshore platform rigs, ships, and boats. This study outlines the major considerations and the steps to take when evaluating asset life extensions for an aging offshore structure (or asset). The study presents an overview on asset management of offshore facilities towards monitoring, safe practices, maintenance, and sustainability. Much of the offshore infrastructure is currently approaching or past its operational life expectancy. Hence, there is a need to have asset management of these offshore assets (or facilities). Recent activities in the oil and gas industry have shown an increasing need for monitoring engagements, such as in shipping, logistics, exploration, drilling, or production. Besides, they demonstrate the model's real-time operation, low memory footprint, robustness to various environmental and operational conditions, and adequacy for deployment in industrial systems. Experimental results show the proposed model outperforms other techniques with 96.4% accuracy, 95.8% precision, 97.2% recall, 96.5% F1-score, and 99.2% AUC. Then, we pre-process the images, design a CNN model in a systematic manner, evaluate its detection and prediction performance, analyze its computational complexity, and compare it with various other models. Experts from Konecranes annotate the collected images in accordance with the rope's condition normal or damaged. We use a camera-based apparatus to photograph the lifting rope's surface, while in operation, and capture the progressive wear-and-tear as well as the more significant degradation in the rope's health state. Specifically, we present a novel vision-based system for detecting damage in synthetic fiber rope images using convolutional neural networks (CNN). Therefore, we propose using deep learning and computer vision methods to automate the process of detecting damaged ropes. This task is time-consuming, prone to human error, halts operation, and may result in the premature disposal of ropes. The health and safety hazards posed by worn crane lifting ropes mandate periodic inspection for damage.
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