Preliminary Results on Condition Monitoring of Fiber Ropes using Automatic Width and Discrete Length Measurements

As the offshore sector moves to deeper waters, fiber ropes have the potential to replace more traditional solutions such as steel wire ropes for deep sea lifting and heave-compensated operations. While steel wire ropes must account for their own weight when determining the maximum depth that a payload can be deployed, fiber ropes such as high modulus polyethylene (HMPE), are more buoyant than their steel counterparts, enabling payloads to be deployed at deeper depths using smaller cranes. For this reason, companies are actively developing fiber rope cranes to be used in industry. The inherent issue with these designs is monitoring the condition of the fiber rope due the multitude of damage mechanisms and condition indicators that exist, therefore determining the time to rupture remains an unsolved problem. To this end, this paper considers the use of computer vision to monitor the width at discrete length sections and use that as a potential condition indicator. Furthermore, the paper describes in detail how OpenCV is applied to detect the contour of the rope to find the width, how the experiment has been performed, as well as other practical experiences from testing a 28mm Dyneema R fiber rope. The experimental results show an exponential relationship between the applied tension and the reduction in width (which was reduced by more than 10% before rupture), and it is believed that if the width can be monitored at discrete sections along the rope over time, the width itself will prove to be a good condition indicator.