A deep learning approach to condition monitoring of cantilever beams via time-frequency extended signatures

摘要

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%. (C) 2018 Elsevier B.V. All rights reserved.