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.

Predicting the motion of ships and floating structures is essential for ensuring economical and environmentally friendly operations in the ocean. In this study, we propose a hybrid encoder–decoder Convolutional Neural Network–Long Short-Term Memory (CNN–LSTM) architecture to predict motions of a moored Floating Production Storage and Offloading (FPSO) vessel under varying sea conditions. The model integrates a CNN for spatial wave-field feature extraction and an LSTM encoder–decoder to capture temporal dependencies in vessel motion. Synthetic datasets were generated using mid-fidelity dynamics simulations of a coupled FPSO–mooring–riser system subjected to wave excitations. Five sea states ranging from calm to severe were considered to evaluate the model’s robustness. A key preprocessing step involved determining the optimal spatial domain for wave field input, and a wave field size of 600 m × 600 m was identified as the most cost-effective configuration while maintaining accuracy. The model was validated using the Root Mean Square Error (RMSE) or relative RMSE (RRMSE). Despite low RRMSE values in low sea states, predictions were noisier due to high-frequency, low-amplitude responses. In contrast, higher sea states yielded more stable predictions despite higher RRMSE values. The proposed method offers high-resolution motion forecasting capability, which can enhance operational safety and energy efficiency of offshore platforms, particularly when integrated with stereo camera-based wave monitoring systems.