 Fault diagnosis of wind turbine based on Long Short-term memory networksJinhao Lei, Chao Liu and Dongxiang Jiang Renewable Energy, 2019, vol. 133, issue C, 422-432 Abstract: Time-series data is widely adopted in condition monitoring and fault diagnosis of wind turbines as well as other energy systems, where long-term dependency is essential to form the classifiable features. To address the issues that the traditional approaches either rely on expert knowledge and handcrafted features or do not fully model long-term dependencies hidden in time-domain signals, this work presents a novel fault diagnosis framework based on an end-to-end Long Short-term Memory (LSTM) model, to learn features directly from multivariate time-series data and capture long-term dependencies through recurrent behaviour and gates mechanism of LSTM. Experimental results on two wind turbine datasets show that our method is able to do fault classification effectively from raw time-series signals collected by single or multiple sensors and outperforms state-of-art approaches. Furthermore, the robustness of the proposed framework is validated through the experiments on small dataset with limited data. Keywords: Wind turbine; Fault diagnosis; Long short-term memory (LSTM) (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:133:y:2019:i:c:p:422-432 DOI: 10.1016/j.renene.2018.10.031 Access Statistics for this article Renewable Energy is currently edited by Soteris A. Kalogirou and Paul Christodoulides More articles in Renewable Energy from Elsevier |
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