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Fault diagnosis of wind turbine gearbox based on wavelet neural network
Author(s) -
Huitao Chen,
Shuangxi Jing,
Wang Xianhui,
Zhiyang Wang
Publication year - 2018
Publication title -
journal of low frequency noise, vibration and active control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.419
H-Index - 25
eISSN - 2048-4046
pISSN - 1461-3484
DOI - 10.1177/1461348418795376
Subject(s) - turbine , fault (geology) , vibration , wavelet , artificial neural network , signal (programming language) , wind power , engineering , control theory (sociology) , computer science , pattern recognition (psychology) , artificial intelligence , acoustics , geology , mechanical engineering , electrical engineering , physics , control (management) , seismology , programming language
In order to monitor the wind turbine gearbox running state effectively, a fault diagnosis method of wind turbine gearbox is put forward based on wavelet neural network. Taking a 1.5 MW wind turbine gearbox as the target of study, the frequency spectrum of vibration signal and the fault mechanism of driving part are analyzed, and the eigenvalues of the frequency domain are extracted. A wavelet neural network model for fault diagnosis of wind turbine gearbox is established, and wavelet neural network is trained by using different feature vectors of fault types. The relationship between fault component and vibration signal is identified, and the vibration fault of wind turbine gearbox is predicted and diagnosed by network model. The analysis results show that the method can diagnose fault and fault pattern recognition of wind turbine gearbox very well.

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