Application of a convolutional neural network for mooring failure identification

A novel application of a convolutional neural network (CNN) for the identification of mooring line failure of a turret-moored FPSO is demonstrated. The CNN was trained on images of the turret horizontal displacement history, simulated for both an intact mooring and a system with one line that had failed. When tested on operational and extreme environments representative of the North West Shelf of Australia, the CNN successfully distinguished between the turret responses associated with the intact and broken mooring. Classification accuracy was found to be lower for relatively benign conditions when the turret offset response was minimal. This was significantly improved through the use of additional hidden layers and retraining. As the CNN does not explicitly utilise metocean data as input, apart from training, it is envisaged that it offers an effective and lower-cost alternative to existing mooring failure detection approaches for the offshore industry. •A convolutional neural network (CNN) is developed to identify mooring line failure.•The CNN is trained on images of the turret displacement offset histories.•The CNN correctly classified the mooring integrity for benign, operational and extreme environments.•Metocean data are not required for classification, except for training the CNN.