Design of Experiments Technique Applied to Artificial Neural Network Models for FPSO Mooring System Analysis

Abstract

Online monitoring of mooring system response for the FPSO platform in any operational condition is so far challenging for machine learning (ML). This paper presents a new dynamic NARX ANN model for time series of mooring tension and a static MLP model for the offset chart prediction of a taut-leg moored FPSO with different working scenarios. A novel method for supervised feature selection of the dataset was applied to determine the most influential design features. Additionally, a design of experiments (DOE) technique was implemented for test matrix creation, simulation, database generation, and supervised selection characteristics in ML. The DOE analysis revealed that the mooring configuration, platform loading condition, and environmental loads alter the platform’s six-degree-of-freedom motion response patterns. These input data were used to predict the mooring tension and the offset chart of the floater. The results include the fair values of statistical error for mooring tension (R2 = 0.8–0.98, E ≈ 1.3–5.7%, RMSE ≈ 6–66 kN) and platform offset (E ≈ 0.1–1 m) prediction when testing the trained models with unseen data representing new operational conditions. Faster convergence can be achieved by adding non-numeric (string) input values to dataset numeric features. Supervised feature selection of the dataset is a step forward in ML to improve prediction accuracy.

Keywords

• artificial neural network (ANN)
• supervised feature selection
• deep learning
• design of experiments (DOE)
• mooring system response prediction
• FPSO platform