Fatigue is one of the main deteriorating mechanisms that affect the safety and reliability of ship structures. Fatigue cracks can appear at various locations along the ship structure and may occur at early stages in the service life of a ship. Inspection, monitoring and/or repair actions are applied to prevent sudden failures of damaged structural components and their associated consequences. However, these actions increase the operational cost of the ship and should be effectively planned during its service life. Due to the presence of significant uncertainties associated with crack initiation and propagation, the planning of such actions should be performed probabilistically. In this paper, a probabilistic approach for inspection, monitoring, and maintenance optimization for ship details under fatigue effects is proposed. Based on the stress profile and the crack geometry at the damaged location, intervention times and types are determined by solving an optimization problem which simultaneously minimizes the life-cycle cost, maximizes the expected service life, and minimizes the expected maintenance delay over the life-cycle. The life-cycle cost includes the cost of inspection, monitoring, and maintenance actions, as well as the cost of failure of the detail. The proposed approach is applied to a side shell detail of a steel ship.
Structural health monitoring (SHM) can be beneficial in reducing epistemic uncertainties associated with fatigue life prediction. For naval ships, available SHM data can be discretized into operational cells, each referring to a certain navigation speed, heading angle, and sea condition.Cell-based approaches for predicting future fatigue life can be applied if monitoring information is known for all cells. However, the available SHM data may populate some, but not all, potential cells. Moreover, since SHM data is only available for a given set of operating conditions, potential changes in climate or operational profiles cannot be accounted for. Accordingly, there is a need for an approach to predict structural responses in unmonitored cells as a function of limited available monitoring data. This paper proposes a methodology to predict the responses of naval vessels in unobserved cells by incorporating data from the limited number of observed cells. The power spectral density (PSD) of the SHM data is fit using generalized fitting functions, based on sea wave spectra, and integrated into the prediction of the PSD for unobserved cells. The proposed methodology enables both spectral and time-domain fatigue methods. The proposed methodology is illustrated on the SHM data from a high speed aluminum catamaran.
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