Random vibration response-based damage diagnosis for a population of composite aerostructures under varying operating conditions and uncertainty: Experimental assessment

The problem of random vibration response-based damage diagnosis (detection & type characterization) for a population of 26 composite aerostructures under varying operating conditions and uncertainty is experimentally investigated. Four damage scenarios are considered in a small sample of the population: Two distinct sizes of a square-shaped delamination and two energy levels of an impact-induced damage. The analysis of the intact (healthy) population indicates rich dynamical information within the range of 0 - 2 kHz with the uncertainty factors, due to manufacturing and experimental setup discrepancies compounded with the temperature and excitation variability, to be strong enough to "mask"the effects of the considered damages, thus leading to a highly challenging damage diagnosis problem. Damage detection is tackled via two unsupervised and robust data-driven vibration-response-only methods: The first is based on the Multiple Model (MM) and the second on the Hyper-Sphere (HS) based frameworks, while damage type characterization is tackled via a hierarchical classification. All methods utilize the Multiple Input Single Output Transmittance Function (MISO-TF) in order to properly compensate for the effects of the varying excitation. The results based on hundreds of test cases indicate impressive detection performance even for the smallest level damage scenarios, with the two detection methods complementing each other, as well as very good damage type characterization, accuracy despite the limited number of damaged aerostructures.