DocumentCode
2317236
Title
The intelligent fault diagnosis of wind turbine gearbox based on artificial neural network
Author
Yang, Shulian ; Li, Wenhai ; Wang, Canlin
Author_Institution
Comput. Dept., ShanDong Inst. of Bus. & Technol., Yantai
fYear
2008
fDate
21-24 April 2008
Firstpage
1327
Lastpage
1330
Abstract
The vibration test system for the gearbox of wind turbine , the wavelet denoising method , the artificial neural networkpsilas essential principles and its features, BP network structures model in the gearbox fault diagnosis are discussed. Tested vibration signals are disposed by the method of wavelet denoising and than as the inputs of BP neural network. By using classical BP neural network, four kinds of typical patterns of gearbox faults have been studied and diagnosed ,and satisfied results have been acquired. The research results indicate that BP neural network have the excellent abilities of parallel distributed processing, self-study, self-adaptation, self-organization, associative memory , and simultaneously its highly non-linear pattern recognition technology is an efficient and feasible tool to solve complicated state identification problems in the gearbox fault diagnosis.
Keywords
backpropagation; fault diagnosis; neural nets; pattern recognition; switchgear; wind turbines; artificial neural network; backpropagation neural network; intelligent fault diagnosis; nonlinear pattern recognition; parallel distributed processing; vibration test system; wavelet denoising; wind turbine gearbox; Artificial intelligence; Artificial neural networks; Associative memory; Distributed processing; Fault diagnosis; Intelligent networks; Neural networks; Noise reduction; System testing; Wind turbines; Artificial Neural Network(ANN); Back Propagation( BP); Denoising; Fault diagnosis; Gearbox; Vibration; wavelet analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Condition Monitoring and Diagnosis, 2008. CMD 2008. International Conference on
Conference_Location
Beijing
Print_ISBN
978-1-4244-1621-9
Electronic_ISBN
978-1-4244-1622-6
Type
conf
DOI
10.1109/CMD.2008.4580221
Filename
4580221
Link To Document