Szczegóły publikacji
Opis bibliograficzny
ART-2 artificial neural networks applications for classification of vibration signals and opera-tional states of wind turbines for intelligent monitoring / Tomasz BARSZCZ, Andrzej BIELECKI, Mateusz Wójcik, Marzena BIELECKA // Diagnostyka / Polskie Towarzystwo Diagnostyki Technicznej ; ISSN 1641-6414. — 2013 — vol. 14 no. 4, s. 21–26. — Bibliogr. s. 25–26, Summ.
Autorzy (4)
- AGHBarszcz Tomasz
- AGHBielecki Andrzej
- Wójcik Mateusz
- AGHBielecka Marzena
Słowa kluczowe
Dane bibliometryczne
| ID BaDAP | 79604 |
|---|---|
| Data dodania do BaDAP | 2014-02-06 |
| Rok publikacji | 2013 |
| Typ publikacji | artykuł w czasopiśmie |
| Otwarty dostęp |  |
| Czasopismo/seria | Diagnostyka |
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. There were several attempts to develop such tools, in most cases based on various classification methods. As the ART neural networks are capable to perform efficient classification and to recognize new states when necessary, they seems to be a proper tool for classification of vibration signals of bearing in gears in wind turbines. The verification of ART-2 networks efficiency in this task is the topic of this paper.
