Temperature Measurements as a Method for Monitoring Ropes
Due to an increasing demand for operation at sea depths as low as 3000 m and under, the use of fibre ropes for offshore application in deep sea lifting and mooring is increasing. Consequently, improved knowledge is required regarding these ropes’ thermo-mechanical properties and how these properties change as the rope is being used. This paper presents a 2D model of heat transport in the axial and radial directions along a 28 mm diameter fibre rope typically used for offshore applications. The model is combined with temperature measurements during heating and cooling of the rope, using both thermocouples and a thermal camera. Measurements are performed both on a new rope and on a used that …
Remaining useful life estimation of HMPE rope during CBOS testing through machine learning
Fibre rope used in cranes for offshore deployment and recovery has significant potential to perform lifts with smaller cranes and vessels to reach depths limited by weight of steel wire rope. Current condition monitoring methods based on manual inspection and time-based and reactive maintenance have significant potential for improvement coupled with more accurate remaining useful life (RUL) prediction. Machine learning has found use as a condition monitoring approach, coupled with vast improvements in data acquisition methods. This paper details data-driven RUL prediction methods based on machine learning algorithms applied on cyclic-bend-over-sheave (CBOS) tests performed on two fibre rope…
Computer vision and thermal monitoring of HMPE fibre rope condition during CBOS testing
Abstract Fibre rope usage in deep sea lifting operations is gaining more prominence in recent times. With rope minimum break loads (MBL) comparable to that of their steel wire counterparts, the use of high modulus polyethylene (HMPE) ropes is seen as a viable option for use in subsea construction cranes. The ropes are worn out during use and visual inspection remains one of the main methods of determining whether a fibre rope is to be retired from use, therefore a natural extension is condition monitoring through computer vision. Creep and temperature are constraining with HMPE ropes and should be monitored continuously, particularly when the rope is cyclically bent over sheaves. Additional…
Condition classification of fibre ropes during cyclic bend over sheave testing using machine learning
Fibre ropes have been shown to be a viable alternative to steel wire rope for offshore lifting operations. Visual inspection remains a common method of fibre rope condition monitoring and has the potential to be further automated by machine learning. This would provide a valuable aid to current inspection frameworks to make more accurate decisions on recertification or retirement of fibre ropes in operational use. Three different machine learning algorithms: decision tree, random forest and support vector machine are compared to classical statistical approaches such as logistic regression, k-nearest neighbours and Naïve-Bayes for condition classification for fibre ropes under cyclic-bend-ov…