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

•A novel deep learning solution for condition monitoring of cantilever beams.•Highest accuracy reaching the theoretical goal of 100%.•Classification using the recent concept of time-frequency extended signatures.•Non-invasive procedure that cancels ambient effects. We introduce with this work a deep...

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Bibliographic Details
Published in:Computers in industry 2019-02, Vol.105, p.177-181
Main Author: Onchis, Habil. Darian M.
Format: Article
Language:English
Subjects:
Damage detection
Deep learning
Natural frequency
Signatures
Time-frequency spectrum
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Summary:•A novel deep learning solution for condition monitoring of cantilever beams.•Highest accuracy reaching the theoretical goal of 100%.•Classification using the recent concept of time-frequency extended signatures.•Non-invasive procedure that cancels ambient effects. 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%.
ISSN:0166-3615
1872-6194
DOI:10.1016/j.compind.2018.12.005