{'fraction_non_alphanumeric': 0.014470677837014471, 'fraction_numerical': 0.0, 'mean_word_length': 5.67005076142132, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Application of damage detection methods using passive reconstruction of impulse response functions

In structural health monitoring (SHM), using only the existing noise has long been an attractive goal. The advances in understanding cross-correlations in ambient noise in the past decade, as well as new understanding in damage indication and other advanced signal processing methods, have continued to drive new research into passive SHM systems. Because passive systems take advantage of the existing noise mechanisms in a structure, offshore wind turbines are a particularly attractive application due to the noise created from the various aerodynamic and wave loading conditions. Two damage detection methods using a passively reconstructed impulse response function, or Green's function, are presented. Damage detection is first studied using the reciprocity of the impulse response functions, where damage introduces new nonlinearities that break down the similarity in the causal and anticausal wave components. Damage detection and localization are then studied using a matched-field processing technique that aims to spatially locate sources that identify a change in the structure. Results from experiments conducted on an aluminium plate and wind turbine blade with simulated damage are also presented.