Automated on-line fault prognosis for wind turbine pitch systems using supervisory control and data acquisition.
- Submitting institution
-
University of Durham
- Unit of assessment
- 12 - Engineering
- Output identifier
- 98859
- Type
- D - Journal article
- DOI
-
10.1049/iet-rpg.2014.0181
- Title of journal
- IET Renewable Power Generation
- Article number
- -
- First page
- 503
- Volume
- 9
- Issue
- 5
- ISSN
- 17521416
- Open access status
- Out of scope for open access requirements
- Month of publication
- -
- Year of publication
- 2015
- URL
-
https://doi.org/10.1049/iet-rpg.2014.0181
- 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
-
2
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper is one of the first to demonstrate the prognostic potential of wind turbine operational data. Subsequently, one author was invited to spend 4 months with DONG Energy in Denmark. DONG have made available their wind turbine data to Durham researchers to continue to develop advanced analytics for improved maintenance strategies. The authors were invited to several industry events to present this work. This has generated further collaborations to be initiated, e.g. with SCADA International and Bruel Kjaer (both Denmark), and the Manufacturing Enterprise Solutions Association, an international body that seeks to improve manufacturing efficiencies through better data analytics.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
