Optimal sensor placement for corrosion induced thickness loss monitoring in ship structures

Current monitoring strategies revolve around proactive maintenance. Corrosion-induced thickness loss (CITL) is one of the most common types of structural deterioration in ship hulls. Recently, Structural Health Monitoring (SHM) has emerged as an appealing alternative that may drive the transition to predictive maintenance. The complexity of ship structures requires that any SHM system must be designed to fully exploit information collected from a limited number of sensors. The aim of this work is to take a step in this direction by framing the design of an SHM system for CITL monitoring in hull structures under the guise of an optimization problem. The problem of detecting the existence of a certain level of thickness loss in hull structural elements is considered and treated using detection theory. The Optimal Sensor Placement (OSP) problem aims to generate strain sensor architectures that maximize the performance of the employed detector and minimize the costs incurred by its erroneous decisions. A computational implementation is considered, and synthetic strain data are generated using a detailed FE model of a reference vessel. Uncertainty is propagated to the data through a Monte Carlo Simulation (MCS). A Genetic Algorithm (GA) is employed to treat the optimization problem and the resultant sensor designs are compared to manually selected alternatives.