Damage detection processes and strategies performed in operative conditions can be gathered into the branch of structural health monitoring. It differs from another fields as the Non-Destructive Examination (NDE) techniques due to the ability to provide data regarding the structure in operative conditions and hopefully before a failure occurs. This need has been addressed with the devel- opment of different methodologies, many of which focus on vibration data ap- proaches. In last decades a wide range of tools has been developed as described in the following thesis. The main effort of this research is to assess the use of wavelet analysis as effective tool to extract damage sensitive features from the vibration data generated by a structure. This scenario is not only present in aerospace framework but involves many applications in mechanical and civil engineering too. We turn our attention also to statistical analysis to get tools able to purge the data from unavoidable randomness and uncertainties. These two pillars - wavelets and statistics - lead us to carry out a joint method where the wavelet analysis are firstly asked to draw the features from the raw data and then a statistical-based tool is demanded to recognize the different structural configurations, namely the undamaged and damaged ones.
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