Abstract Summary
The study aims at exploring the capabilities and performance limits of advanced robust approaches, such as those based on the novel Multiple Model (MM) and the Hyper-Sphere (HS) principles, for random vibration response-based damage detection and characterization for a population of composite aerostructures under varying operating conditions and uncertainty. The study is based on Monte Carlo simulations using digital, Abaqus based, models thus allowing for maximum flexibility in experimentation. Each aerostructure is a Carbon-Epoxy square hollow beam of length equal to 2570 mm and varying cross section. It consists of two main parts, the web and the skin, which are assembled using adhesive and models the tail boom of a UAV. The population is characterized by material and manufacturing uncertainty, modeled via ±10% variation around the nominal material property values. Additional uncertainty is caused by temperature variability (within the -55…71 ℃ range) as well as force excitation profile uncertainty due to the employment of excitation realizations related to two distinct stochastic profiles within the 0-2 kHz frequency range. Two types of early-stage damage are considered: Debonding and Delamination, each one of two different sizes and at one of two distinct locations; hence a total of 8 damage scenarios. Debonding is simulated via adhesive stiffness degradation and delamination via corresponding degradation on all skin layers. Both damage types induce small effects on the structural dynamics which are largely masked by those due to varying operating conditions and uncertainty, thus leading to a challenging diagnosis problem. Multiple Model (MM) and Hyper-Sphere (HS) based advanced data-driven robust methods employing Multiple Input Single Output (MISO) Transmittance Function representations of the structural dynamics estimated as stochastic MISO AutoRegressive with eXogenous excitation (MISO-TF-ARX) models are employed. This model type is selected as it achieves elimination of excitation effects, while the MISO-TF-ARX model parameter vector, or Principal Component Analysis (PCA) reduced versions, constitute the methods’ feature within which distinct approximations of the healthy subspace are constructed (Baseline Phase). Once a fresh set of vibration response signals is obtained, a corresponding MISO-TF-ARX model corresponding to a point in the feature space is estimated, and damage detection is based on examining a proper distance metric of it from the healthy subspace (Inspection Phase). Once damage is detected, its characterization, in terms of damage type, location, and size, is attempted based on a supervised hierarchical classification scheme that employs an angle-based approach within the feature space. Damage detection is comprehensively assessed with a population of 18 healthy aerostructures (9 employed in the Baseline and 9 in the Inspection Phases) and 72 damaged structures, different from their Baseline counterparts, while a total of 1323 Monte Carlo numerical experiments are run for the population. The methods achieve high detection performance, reaching a 100% correct detection rate for 0% false alarm rate for the best performing PCA-HS-TF method despite the few Baseline experiments. Damage characterization results are also very promising, characterized by 80% correct classification rate.
