Szczegóły publikacji
Opis bibliograficzny
ART-2 artificial neural networks applications for classification of vibration signals and operational states of wind turbines for intelligent monitoring / Tomasz BARSZCZ, Andrzej BIELECKI, Mateusz Wójcik, Marzena BIELECKA // W: Advances in Condition Monitoring of Machinery in Non-stationary Operations : proceedings of the third international conference on Condition Monitoring of Machinery in Non-stationary Operations CMMNO 2013 / eds. Giorgio Dalpiaz, [et al.]. — Berlin ; Heidelberg : Springer-Verlag, cop. 2014. — (Lecture Notes in Mechanical Engineering ; ISSN 2195-4356). — ISBN: 978-3-642-39347-1; e-ISBN: 978-3-642-39348-8. — S. 679–688. — Bibliogr. s. 687–688, Abstr.
Autorzy (4)
- AGHBarszcz Tomasz
- AGHBielecki Andrzej
- Wójcik Mateusz
- AGHBielecka Marzena
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 87985 |
|---|---|
| Data dodania do BaDAP | 2015-02-24 |
| DOI | 10.1007/978-3-642-39348-8_58 |
| Rok publikacji | 2014 |
| Typ publikacji | materiały konferencyjne (aut.) |
| Otwarty dostęp |  |
| Konferencja | Third international conference on Condition Monitoring of Machinery in Non-Stationary Operations |
| Czasopismo/seria | Lecture Notes in Mechanical Engineering |
Abstract
In recent years wind energy is the fastest growing branch of the power generation industry. The largest cost for the wind turbine is its maintenance. A common technique to decrease this cost is a remote monitoring based on vibration analysis. Growing number of monitored turbines requires an automated way of support for diagnostic experts. As full fault detection and identification is still a very challenging task, it is necessary to prepare an "early warning" tool, which would focus the attention on cases which are potentially dangerous.
