A failure in the mooring line of a platform, if not detected quickly, can cause a riser system failure, extended production downtime, or even environmental damages. Therefore, integrity management and timely detection of mooring failure for floating platforms are critical. In this paper, we propose a new model for an ANN-based mooring failure detection system. The proposal’s idea is to train a Multilayer Perceptron (MLP) to estimate the platform’s future motion based on its motion’s temporal data without failure. A classifier then indicates whether or not there is a failure in the mooring system based on the difference between the predicted and the measured motion. The results with several tests of the implemented system show that our proposal can correctly predict the motion of the platform in most environmental conditions. The system shows a precision, accuracy and F1-score of 99.88%, 99.99% and 99.94%, respectively, for detecting changes in platform motion in near real-time, quickly signaling a possible breakage of mooring lines.

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