Application of a convolutional neural network for mooring failure identification

Chris Janas, Ian Milne, James Whelan

Research output: Contribution to journalArticlepeer-review

3 Citations (Web of Science)


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.
Original languageEnglish
Article number109119
JournalOcean Engineering
Early online date18 May 2021
Publication statusPublished - 15 Jul 2021


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