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Title: Comparison of different modelling approaches of drive train temperature for the purposes of wind turbine failure detection
Authors: Tautz-Weinert, Jannis
Watson, Simon J.
Issue Date: 2016
Publisher: © IOP
Citation: TAUTZ-WEINERT, J. and WATSON, S.J., 2016. Comparison of different modelling approaches of drive train temperature for the purposes of wind turbine failure detection. Journal of Physics: Conference Series, 753 (072014)
Abstract: Effective condition monitoring techniques for wind turbines are needed to improve maintenance processes and reduce operational costs. Normal behaviour modelling of temperatures with information from other sensors can help to detect wear processes in drive trains. In a case study, modelling of bearing and generator temperatures is investigated with operational data from the SCADA systems of more than 100 turbines. The focus is here on automated training and testing on a farm level to enable an on-line system, which will detect failures without human interpretation. Modelling based on linear combinations, artificial neural networks, adaptive neuro-fuzzy inference systems, support vector machines and Gaussian process regression is compared. The selection of suitable modelling inputs is discussed with cross-correlation analyses and a sensitivity study, which reveals that the investigated modelling techniques react in different ways to an increased number of inputs. The case study highlights advantages of modelling with linear combinations and artificial neural networks in a feedforward configuration.
Description: This is an Open Access article. It is published by IOP under the Creative Commons Attribution 3.0 Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/3.0/
Sponsor: This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 642108 (Advanced Wind Energy Systems Operation and Maintenance Expertise, http://awesome-h2020.eu/).
Version: Published
DOI: 10.1088/1742-6596/753/7/072014
URI: https://dspace.lboro.ac.uk/2134/22981
Publisher Link: http://dx.doi.org/10.1088/1742-6596/753/7/072014
ISSN: 1742-6588
Appears in Collections:Published Articles (Mechanical, Electrical and Manufacturing Engineering)

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