This paper presents a review of different condition monitoring technologies for fiber ropes. Specifically, it presents an overview of the articles and patents on the subject, ranging from the early 70’s up until today with the state of the art. Experimental results are also included and discussed in a conditionmonitoring context,where failuremechanisms and changes in physical parameters give improved insight into the degradation process of fiber ropes. From this review, it is found that automatic width measurement has received surprisingly little attention, and might be a future direction for the development of a continuous condition monitoring system for synthetic fiber ropes.

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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-over-sheave (CBOS) testing. By measuring the rope global elongation throughout the CBOS tests, a binary classification system has been used to label recorded samples as healthy or close to rupture. Predictions are made on one rope through leave-one-out cross validation. The models are then assessed through calculating the accuracy, probability of detection, probability of false alarm and Matthew’s Correlation Coefficient, and ranked based on the results. The results show that both machine learning and classical statistical methods are effective options for condition classification of fibre ropes under CBOS regimes. Typical values for Matthews Correlation Coefficient (MCC) were shown to exceed 0.8 for the best performing methods.

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