Fukunaga-Koontz feature transformation for statistical structural damage detection and hierarchical neuro-fuzzy damage localisation
- Submitting institution
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University of Aberdeen
- Unit of assessment
- 12 - Engineering
- Output identifier
- 105639029
- Type
- D - Journal article
- DOI
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10.1016/j.jsv.2017.03.048
- Title of journal
- Journal of Sound and Vibration
- Article number
- -
- First page
- 329
- Volume
- 400
- Issue
- -
- ISSN
- 0022-460X
- Open access status
- Compliant
- Month of publication
- April
- Year of publication
- 2017
- 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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1
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- An outcome of a project funded by The Lloyd’s Register Foundation (‘The LRF Centre for Safety and Reliability Engineering’) that conceptualizes, develops and validates experimentally a methodology for improving damage detection algorithms by bringing the Fukunaga-Koontz transformation to structural health monitoring. The paper is included in a review of state-of-the-art and future challenges in structural health monitoring and damage detection (Vagnoli et al 2018).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
