Abstract
We introduce with this work a deep learning approach for non-invasive condition monitoring of cantilever beams. The deep learning classifier is used to recognize a damaged or undamaged beam via time-frequency extended signatures. These signatures are the distributions over several measurements of the natural frequencies extracted from the refined time-frequency adaptive spectrum of vibrating beams. The test results showed that we are able to cancel ambient effects like the temperature and to obtain a high accuracy of the results which for the considered cases reach 100%.
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