Digital Damage Fingerprints (DDF) and its application in quantitative damage identification

One challenge for structural damage identification using active sensor network is how to appropriately define and extract feature components from raw signals, so as to faithfully describe the damage to be identified. Motivated by this, a signal processing and interpretation technique based on a novel concept, Digital Damage Fingerprints (DDF), was developed in this study, particularly for the purpose of quantitative identification of structural damage. Such an approach is able to efficiently identify and digitise characteristics in signals acquired from active sensor network, and consequently quantify a complicated structure using concise yet essential information. For validation, the technique was then applied to the development of Damage Parameters Databases (DPDs) and online quantitative identification of through-hole and delamination damage in CF/EP (T650/F584) composite structures, under assistance of an artificial neural algorithm. The results exhibit excellent performance of DDF technique in system pattern recognition.