A numerically-enhanced machine learning approach to damage diagnosis using a Lamb wave sensing network
- Submitting institution
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The University of Sheffield
- Unit of assessment
- 12 - Engineering
- Output identifier
- 2692
- Type
- D - Journal article
- DOI
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10.1016/j.jsv.2014.04.059
- Title of journal
- Journal of Sound and Vibration
- Article number
- -
- First page
- 4499
- Volume
- 333
- Issue
- 19
- ISSN
- 0022-460X
- Open access status
- Out of scope for open access requirements
- Month of publication
- May
- Year of publication
- 2014
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
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2
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper presents a novel approach, based around the amalgamation of data- and model-based approaches, that permits damage diagnoses to be made on high-value engineering structures despite there being a lack of damage-scenario data. The work was validated on an aircraft skin structure and was instrumental in the successful Marie Skłodowska-Curie Actions Innovative Training Network “Intelligent Prognostics and Health Management in Composite Structures”. The aim of this network is to leverage the hybrid approach outlined in the paper in order to drastically reduce the cost of maintaining composite engineering structures (contact: Associate Professor, University of Granada, Spain).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -