Szczegóły publikacji
Opis bibliograficzny
Vertical axis wind turbine states classification by an ART-2 neural network with a stereographic projection as a signal normalization / Tomasz BARSZCZ, Andrzej BIELECKI, Marzena BIELECKA, Mateusz WÓJCIK, Mirosław Włuka // W: Advances in Condition Monitoring of Machinery in Non-Stationary Operations : proceedings of the fourth international conference on Condition Monitoring of Machinery in Non-Stationary Operations CMMNO'2014, Lyon, France, December 15–17 / eds. Fakher Chaari, [et al.]. — Switzerland : Springer International Publishing, cop. 2016. — (Applied Condition Monitoring ; ISSN 2363-698X ; vol. 4). — ISBN: 978-3-319-20462-8; e-ISBN: 978-3-319-20463-5. — S. 265–275. — Bibliogr. s. 274–275, Abstr.
Autorzy (5)
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 91992 |
|---|---|
| Data dodania do BaDAP | 2015-10-05 |
| DOI | 10.1007/978-3-319-20463-5_20 |
| Rok publikacji | 2016 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Wydawca | Springer |
| Konferencja | 4th International Conference on Condition Monitoring of Machinery in Non-Stationary Operations |
| Czasopismo/seria | Applied Condition Monitoring |
Abstract
A common technique used to decrease a cost of wind turbine maintenance is a remote monitoring. Apart from the development of several advanced diagnostic methods for wind turbines there is a need to prepare an early warning tool which would work continuously in real-time and focus the attention on potentially dangerous cases. A research using the resonance neural networks made so far by the authors gave positive results. Systems based on the ART-2 networks were able to perform a classification of operational states of a horizontal axis wind turbine. In this paper the innovative idea of using the ART-2 network is applied to data from vertical axis wind turbines. The system, which were composed by ART-2 and new signal normalization procedures based on a stereographic projection, was implemented and tested. Simulations of a system operation showed that it is capable to perform an efficient state classification.
