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An adaptable physics-informed fault diagnosis approach via hybrid signal processing and transferable feature learning for structural/machinery health monitoring

Crossref DOI link: https://doi.org/10.1007/s11760-024-03529-y

Published Online: 2024-09-27

Published Print: 2024-12

Update policy: https://doi.org/10.1007/springer_crossmark_policy

Authors

Zarchi, Milad

Shahgholi, Majid

Tee, Kong Fah
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Text and Data Mining valid from 2024-09-27

Version of Record valid from 2024-09-27
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Article History

Received: 29 November 2022

Revised: 2 April 2024

Accepted: 20 August 2024

First Online: 27 September 2024

Declarations

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: The authors declare no competing interests.

: The authors consent for publication.

: The authors demonstrate that they have adhered to the accepted ethical standards of a genuine research study. They consent to participate.

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