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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.

Fiber ropes are steadily gaining in popularity for offshore lifting purposes. One limiting factor is many fibers’ low tolerance for high temperatures. Measurements of rope temperature and changes in thermo-physical properties are therefore highly relevant, a task which may be performed using an infrared camera. Chemometrics is one tool among the many techniques available for image processing. The present paper details results from applying chemometrics to infrared images obtained from recent cyclic-bend-over-sheave testing. It is shown how this tool contributes to separating the various phenomena going on, like changes in thermal properties, vertical rope movement, surface degradation, and rope twist. A brief discussion on the applicability for real-life monitoring is also given.

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