A Novel Image-Based Framework for Process Monitoring and Fault Diagnosis of Mooring Lines

Rabeea Jaffari, Manzoor Ahmed Hashmani, Hasmi Taib, Nasir Abdullah, Syed Sajjad Hussain Rizvi


Mooring lines play a vital role in offshore marine operations by limiting marine vessels' free movement on the water. Problems in mooring lines must therefore be detected and solved beforehand to guarantee successful and loss-free offshore operations. Sudden and undetected failure of mooring lines has caused significant disruptions of operations. Many studies focus on mooring lines failure and give ts comprehensive description. Available solutions to detect faults in mooring lines comprise the conventional line tension measurements. Circle- based GPS approaches are less accurate, unreliable, expensive, difficult to install, and maintain. They usually have a limited lifetime or the most recent Deep Learning-based techniques, which are computationally costly, data-hungry, and inefficient to localize thin mooring lines from images. In this research study, we present MoorFDM: Mooring Fault Diagnosis and Monitoring- a novel image-based framework for process monitoring and fault diagnosis of thin mooring lines. MoorFDM monitors and detects faults well in the thin mooring lines from images using our novel line pooling algorithm. Our proposed framework is validated using web-based light mooring line images and images from an oil and gas company with accurate results.  



Keywords: mooring lines, process monitoring, and fault diagnosis (PFD), thin objects detection, computer vision, deep learning.

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