Data-driven ship fatigue assessment based on pitch and heave motions

Paper i proceeding, 2023

Ocean-crossing ship structures continuously suffer from wave-induced loads when sailing at sea. The encountered wave loads cause significant variations in ship structural stresses, leading to accumulated fatigue damage. It is common today to use the spectral method for direct fatigue calculation when evaluating ship fatigue, where large inherent uncertainties still exist. This paper investigates the machine learning technique to establish model for a 2800TEU container vessel fatigue assessment. The measurement data of three years cross-Atlantic sailing demonstrates and validates the machine learning model. In this investigation, the motions of the ship are used as inputs to build machine learning model. The fatigue damage amounts predicted using machine learning model were compared with those obtained from full-scale measurements and direct fatigue calculation. The pros and cons of the methods are compared in terms of capability, robustness, and accuracy of the prediction.