A pattern recognition approach to acoustic emission data originating from fatigue of wind turbine blades
- Submitting institution
-
Brunel University London
- Unit of assessment
- 12 - Engineering
- Output identifier
- 206-182178-8468
- Type
- D - Journal article
- DOI
-
10.3390/s17112507
- Title of journal
- Sensors
- Article number
- 2507
- First page
- -
- Volume
- 17
- Issue
- 11
- ISSN
- 1424-2818
- Open access status
- Compliant
- Month of publication
- November
- Year of publication
- 2017
- URL
-
http://www.mdpi.com/1424-8220/17/11/2507
- 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
-
3
- Research group(s)
-
2 - Applied Mechanics & Structures
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The Acoustic Emission signals measured during a fatigue test for a wind turbine blade are analyzed in order to obtain the correlation between the fracture mechanisms in composite materials and AE signal characteristics. A set of signal characteristic features are correlated with failure modes such as matrix cracking, delamination and debonding using a robust clustering algorithm.
It is as far as known to the authors, the first published paper studying the AE signals in correlation to the fracture mechanisms for a blade in a load controlled fatigue program, as opposed to small scale laboratory tests.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -