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 :
بازگشت