Monitoring and Identifying Wind Turbine Generator Bearing Faults Using Deep Belief Network and EWMA Control Charts
Wind turbines are widely installed as the new source of cleaner energy production. Dynamic and random stress imposed on the generator bearing of a wind turbine may lead to overheating and failure. In this paper, a data-driven approach for condition monitoring of generator bearings using temporal tem...
Guardado en:
Autores principales: | Huajin Li, Jiahao Deng, Shuang Yuan, Peng Feng, Dimuthu D. K. Arachchige |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Frontiers Media S.A.
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/499470d9163f4ee49543bfe6aa9e1ab9 |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
Valor em Risco (VaR) utilizando modelos de previsão de volatilidade: EWMA, GARCH e Volatilidade Estocástica
por: Fernando Caio Galdi, et al.
Publicado: (2007) -
Rolling bearing fault detection based on vibration signal analysis and cumulative sum control chart
por: Mohammed Jawad Saja, et al.
Publicado: (2021) -
Control Chart Patterns Recognition Based on Optimized Deep Belief Neural Network and Data Information Enhancement
por: Hongyan Chu, et al.
Publicado: (2020) -
Development and testing of a three‐dimensional ballistics model for bat strikes on wind turbines
por: Shivendra Prakash, et al.
Publicado: (2021) -
SCADA Data-Based Working Condition Classification for Condition Assessment of Wind Turbine Main Transmission System
por: Huanguo Chen, et al.
Publicado: (2021)