Abstract

The use of an Artificial Neural Network (ANN) for detection of mooring line failure has been a growing subject of discussion over the past several years. Sidarta et al. [6, 8, 12] have presented papers on the detection of mooring line failure of a moored vessel by monitoring shifts in the low frequency periods, mean yaw angles as a function of vessel positions, mass and added mass.

An ANN model has been trained using MLTSIM hydrodynamic simulations based on information from the early stages of the project. The restoring forces and moments from mooring lines, risers and umbilicals have been solved using catenary equations to significantly reduce the computational time to generate the ANN training data. This paper presents the evaluation of this ANN model using fully coupled OrcaFlex hydrodynamic simulations, based on the latest information of the project. The results of this evaluation demonstrate the tolerance of the trained ANN model as it can properly function when tested using time series of vessel motions from the fully coupled OrcaFlex hydrodynamic simulations. Furthermore, although the ANN model has been trained using simulations with a completely removed line, the trained model can still function when tested with simulations of a line broken at the bottom. These give affirmation that the ANN model can tolerate the differences that exist between the test and training data.

Sensitivity of the polyester line stiffness has also been performed using fully coupled OrcaFlex hydrodynamic simulations, and the computed time series of vessel motions have been used to test the ANN model. The ANN model can deal with some level of differences between the sensitivity tests and training data. However, sensitivity tests of the polyester line stiffness to model aging lines has posed a real challenge to the ANN model as its prediction accuracy has decreased significantly. This paper presents an adaptive method that can be implemented such that the ANN model can adapt to relatively new conditions that are quite different from the training data and maintain the accuracy of its prediction. With this approach, an existing ANN model that has been trained under certain assumptions of the system can still function although the behavior of the system has drifted away from those assumptions.

This phenomenon may have similarity with a possible reality that measured behavior in the field can be somewhat different from numerical simulations. This adaptive method has a potential for addressing this issue such that a simulation trained ANN model can maintain its expected accuracy although dealing with different conditions from the training data. If successful, this is a good cost saving scenario that an ANN model adapts to some degree to relatively new and different conditions before the differences become too much to handle and the only solution is to retrain the model.

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