A novel condition monitoring method of wind turbines based on long short-term memory neural network
- Submitting institution
-
Brunel University London
- Unit of assessment
- 12 - Engineering
- Output identifier
- 337-209633-14545
- Type
- D - Journal article
- DOI
-
10.3390/en12183411
- Title of journal
- Energies
- Article number
- 3411
- First page
- -
- Volume
- 12
- Issue
- 18
- ISSN
- 1996-1073
- Open access status
- Compliant
- Month of publication
- September
- Year of publication
- 2019
- URL
-
https://www.mdpi.com/1996-1073/12/18/3411/pdf
- 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
-
4
- Research group(s)
-
2 - Applied Mechanics & Structures
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper resulted from a close industrial collaboration with ESI and TWI to develop a software platform for digital twin of wind turbine monitoring. Currently ESI is looking into commercialising the AI Platform. This development has led to more applications eg, Smartbridge, UltraHiT and TidalTwin which are all funded by InnovateUK.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -