Machine Learning-Based Mooring Failure Detection for FPSOs: A Two-Step ANN Approach

This study presents a two-step artificial neural network (ANN) approach for detecting mooring failures in a spread-moored floating production storage and offloading (FPSO) vessel using platform motion data. Synthetic statistical data generated from time-domain simulations were utilized as input features. The first-step ANN determines whether the mooring system is intact or a failure has occurred within a specific mooring group. If a failure is detected, the second-step ANN identifies the exact failed mooring line within the group. Hyperparameter optimization was performed using Bayesian and random search methods, and multiple input variable sets were evaluated. The results indicate that the mean values of platform motions, particularly surge and yaw, play a crucial role in accurately identifying mooring failures. Additionally, selecting the top 10 features based on mutual information can be a way to improve detection accuracy. The proposed two-step ANN approach outperformed the single-step ANN method, achieving higher classification accuracy and reducing misclassification between mooring lines. These findings demonstrate the potential of machine learning for near-real-time mooring integrity monitoring, offering a practical and efficient alternative to traditional inspection methods.