Interest has been expressed during the past few years toward incorporating structural health monitoring (SHM) systems in ship hull structures for detecting damages that cause significant load-carrying reductions and subsequent load redistributions. The guiding principle of the damage identification strategy considered in this work is based upon measuring, through a limited number of sensors, the static strain redistributions caused by an extensive damage. The problem is tackled as a statistical pattern recognition one, and therefore, methods sourcing from machine learning (ML) are applied. The SHM strategy is both virtually and experimentally applied to a thin-walled prismatic geometry that represents an idealized hull form solely subjected to principal bending stresses (sagging/hogging). Damage modes causing extensive stress redistribution, are abstractly represented by a circular discontinuity. The damage identification problem is treated in a hierarchical order, initialized by damage detection and moving to an increasingly more localized prediction of the damage location. Training datasets for the ML tools are generated from numerical finite element simulations. Measurement uncertainty is propagated in the theoretical strains by information inferred from experimental data. Two different sensor architectures were assessed. An experimental programme is performed for testing the accuracy of the proposed damage identification strategy, yielding promising results and providing valuable insights.
Current maintenance procedures for ship hulls are based around a series of time-fixed on-site surveys. The vision for the future of the maritime industry revolves around condition-based hull structural maintenance. The methods and techniques associated with realizing this vision fall within the field of Structural Health Monitoring. The goal of this article is to present the opportunities offered by the design and implementation of hull SHM systems which will enable the transition towards predictive maintenance. The primary focus will be to discuss the different aspects of such a framework as well as potential challenges associated with its development and implementation.
Damage identification in ship structures is traditionally performed through on-site inspections. In this work, a first step is made towards assessing an in-line with operation ship hull Structural Health Monitoring system by registering onboard sensor data. Specifically, an optimization-based approach is proposed for solving the inverse problem for damage identification through processing static response data. Idealized geometry and loading conditions are considered for the deck and shell plating. Damage is abstractly represented as a single circular hole randomly located within the defined domain. Strain readings representing onboard measured data are provided by a FE model developed for this purpose. These correspond to zero-strain paths for each considered case: axial strains along the ship’s neutral axis on the side shells and shear strains along the deck’s centerline. Damage detection amounts to predicting its location, essentially considered the design variable of an optimization problem seeking to minimize an error function between strains measured for various damage scenarios and an indicative target scenario. Three established optimization algorithms are used for this task: a gradient-based, a Genetic Algorithm-based and a statistics-based method (Design of Experiments and Response Surface Methodology). Results indicate that the gradient and GA based approaches are more efficient while the less efficient statistics-based approach proved less computationally demanding.
Fatigue crack growth is one of the most common types of deterioration in metal structures with significant implications on their reliability. Recent advances in Structural Health Monitoring (SHM) have motivated the use of structural response data to predict future crack growth under uncertainty, in order to enable a transition towards predictive maintenance. Accurately representing different sources of uncertainty in stochastic crack growth (SCG) processes is a non-trivial task. The present work builds on previous research on physics-based SCG modeling under both material and loadrelated uncertainty. The aim here is to construct computationally efficient, probabilistic surrogate models for SCG processes that successfully encode these different sources of uncertainty. An approach inspired by latent variable modeling is employed that utilizes Gaussian Process (GP) regression models to enable the surrogates to be used to generate prior distributions for different Bayesian SHM tasks as the application of interest. Implementation is carried out in a numerical setting and model performance is assessed for two fundamental crack SHM problems; namely crack length monitoring (damage quantification) and crack growth monitoring (damage prognosis).
In the recent years, interest has been expressed towards incorporating Structural Health Monitoring (SHM) systems to ship hulls in order to transition from preventive to predictive maintenance procedures. In this work, an initial approach is undertaken to investigate the capabilities of a model-based method treating damage identification as an optimization problem solved using a genetic algorithm. An idealization of the hull structure is considered based on hull girder theory that allows for lab scale experimental testing. Specifically, a box girder is considered with a circular discontinuity as the generalized damage that causes extensive stress redistribution, replicating the effect of hull damage modes of interest. A three-point bending load case is considered to emulate still water bending loads. Damage is considered to exist, and the goal of the proposed strategy is to provide a prediction on its location and magnitude (level 2 SHM). This is achieved using strain measurements obtained from sensors located on theoretical zero-strain directions as inputs to the optimization scheme treating the damage identification problem. Results from both assessment strategies highlighted the influence of measurement-related uncertainties on the method’s predictive capabilities.
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