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The harsh and uncertain operational profile that characterizes the service life of a ship vessel demands for developing robust methods able to monitor the degrada-tion of its structural integrity. Load recognition is a central part in condition moni-toring of ship hull structures and when realized both diagnostic and prognostic ca-pabilities will be enabled. In this work a structural health monitoring framework is proposed under the conceptual basis of digital twining. The present approach em-phasizes the inverse identification of loads acting on the ship’s hull by exploiting readings from strain sensors and is cast in a Bayesian setting. The loading profile is then applied to the numerical model of the subject vessel for the estimation of the stress field at critical areas prone to fatigue damage, i.e. hot spots. The pro-posed framework is evaluated by synthetic strain data derived from a finite ele-ment (FE) model of a containership. The results demonstrate the feasibility of the proposed approach for addressing the load identification problem and acts as a starting point for the development of a structural digital twin that will allow for monitoring fatigue accumulation in ship hull structures.
Panagiotopoulos et al. (Sat,) studied this question.