Vibration-based Damage Diagnosis for a Population of Composite Structures Under Uncertainty via Hyper-Sphere and Transfer Component Analysis Based Methods

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The problem of vibration-based damage diagnosis, including the detection and type characterization of damages in a population of nominally identical composite structures under operational uncertainty is investigated. This is an important problem for various types of fleets, such as aircraft, drones, fixed wing UAVs and others, which becomes highly challenging if the variability in the composite materials and the manufacturing of the population is significant, while the diagnosis of early-stage damages is pursued. Such a population of 27 composite coupons subjecting to operating variability due to alterations in the temperature, the excitation and the experimental setup is considered in this study. Furthermore, early-stage delamination and impact-induced damages are implemented in 10 of the coupons. Damage diagnosis is herein attempted via two data-driven methods that may account for various types of uncertainty, which are founded on a number of stochastic Multiple-Input Single-Output Transmittance Function AutoRegressive with eXogenous pseudo-eXcitation (MISO-TF-ARX) models. The first employs Hyper Spheres for the representation of the healthy subspace under uncertainty using the parameters of the MISO-TF-ARX models and thus abbreviated as HS-MISO-TF, while the other is based on the Transfer Component Analysis of these parameters, abbreviated as TCA-MISO-TF. The methods’ damage diagnosis performance is experimentally assessed using the above composite coupons and a high number of 7 500 inspection test cases for damage detection, whereas damage characterization is based on 450 test cases. The damage detection and characterization results are presented through Receiver Operating Characteristics curves and confusion matrices, respectively. The obtained results indicate adequate damage detection with the HS-MISO-TF method to achieve 82.1% correct detection for 5% of false alarms, with the TCA-MISO-TF following with 76.4% correct detection. Damage characterization is remarkable with the HS-MISO-TF to correctly characterizing the considered damage type for the 100% of the considered test cases and the TCA-MISO-TF following with 92.2%.