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An Artificial Neural Network Approach for Early Fault Detection of Gearbox Bearings
KTH, School of Electrical Engineering (EES), Electromagnetic Engineering.ORCID iD: 0000-0003-4763-9429
2015 (English)In: IEEE Transactions on Smart Grid, ISSN 1949-3053, E-ISSN 1949-3061, Vol. 6, no 2, 980-987 p.Article in journal (Refereed) Published
Abstract [en]

Gearbox has proven to be a major contributor toward downtime in wind turbines. The majority of failures in the gearbox originate from the gearbox bearings. An early indication of possible wear and tear in the gearbox bearings may be used for effective predictive maintenance, thereby reducing the overall cost of maintenance. This paper introduces a self-evolving maintenance scheduler framework for maintenance management of wind turbines. Furthermore, an artificial neural network (ANN)-based condition monitoring approach using data from supervisory control and data acquisition system is proposed. The ANN-based condition monitoring approach is applied to gearbox bearings with real data from onshore wind turbines, rated 2 MW, and located in the south of Sweden. The results demonstrate that the proposed ANN-based condition monitoring approach is capable of indicating severe damage in the components being monitored in advance.

Place, publisher, year, edition, pages
2015. Vol. 6, no 2, 980-987 p.
Keyword [en]
Artificial neural networks (ANN), condition monitoring system (CMS), maintenance management, smart grid, supervisory control and data acquisition systems (SCADAs), wind power generation
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-163991DOI: 10.1109/TSG.2014.2386305ISI: 000350338100054Scopus ID: 2-s2.0-84924077995OAI: oai:DiVA.org:kth-163991DiVA: diva2:807472
Note

QC 20150423

Available from: 2015-04-23 Created: 2015-04-13 Last updated: 2017-12-04Bibliographically approved

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Tjernberg, Lina Bertling

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