An Indirect Measurement Methodology to Identify Fatigue Damage in the Structure of a 2800teu Container Ship

Ships endure fatigue damage from continuous wave-induced stress. The spectral method, despite being a standard assessment tool, is fraught with uncertainties. However, a large segment of today’s maritime vessels abstains from embedding continuous, life-cycle-spanning sensor systems to monitor fatigue damage accumulation. This lacuna precipitates pronounced ambiguities in maintenance prediction, highlighting the urgent need for a rigorously systematic approach to address this knowledge void. To address these issues, this paper introduces a machine learning-based indirect measurement method for evaluating fatigue damage in a 2800TEU container vessel. Utilizing three years of cross-Atlantic voyage data, the study aims to predict fatigue damage more accurately. Our method, which leverages available navigational and environmental data, circumvents the need for intricate sensors. We benchmark our model’s predictions against full-scale measurements and conventional approaches, scrutinizing the accuracy and reliability of each. This indirect strategy not only promises to enhance maritime safety through a more lucid understanding of fatigue accumulation but also supports maintenance planning by estimating long-term fatigue impact. This research posits a simpler yet potentially more efficacious alternative for the surveillance and management of fatigue in maritime vessels.