Robustness of Structural Health Monitoring Systems for Offshore Energy Structures based on Damage Characteristics

Offshore energy structures, such as fixed and floating wind turbines, tidal turbines and wave energy converters, are exposed to random loads coming from different sources such as wind and wave forces, tides, temperature forces, and ice forces. Recent advances in structural health monitoring (SHM) offer many unique opportunities to assess the structural integrity of offshore energy infrastructure in extreme climatic environments. The process of SHM generally involves the use of a system of sensors mounted on a monitored structure to collect data on the structure's performance and then extract damage sensitive features to determine the current state of system health. Over the past decades, most of the research in SHM has focused on developing new sensing technologies, signal processing systems, and damage detection algorithms. However, there have been very few studies to date on assessing the robustness of SHM systems with respect to damage detection, localization, and quantification. Moreover, most of the SHM performance analysis methods rely only on an indicator of probability of detection (POD), that is an index used in nondestructive testing (NDT) to evaluate the probability of detecting a damage as a function of its size. For SHM systems, the replicability of POD is influenced by the environment, measurement noise, and the deterioration of sensors and instruments. Therefore, the distribution of POD is often determined by performing an extensive number of experiments with existing damage of varying magnitude. This paper aims to reduce the volume of experimental data as well as the uncertainties associated with POD prediction by using a model-assisted probability of detection (MAPOD) approach. We develop a method to evaluate the performance of SHM systems in terms of damage detection, localization, and severity assessment in a unified manner. In addition to POD, the probability of accurate localization (POAL) and the probability of accurate assessment (POAA) are incorporated into the analysis. The proposed method and the limit state functions are applied to a bottom-fixed offshore energy structure equipped with a vibration based SHM system.