Asynchronous Motor Fault Diagnosis Based on Wavelet Neural Network

According to the mapping relationship between the common symptoms of fault in the asynchronous motor and fault mode, this paper established asynchronous motor fault diagnosis model by using the wavelet neural network (WNN). The model adopts the conjugate gradient descent algorithm, which is optimize...

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Bibliographic Details
Published in2009 International Conference on Information Engineering and Computer Science pp. 1 - 4
Main Authors Guizhen Zhou, Guorong Liu, Yiping Luo
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.12.2009
Subjects
Defense industry
Fault detection
Fault diagnosis
Industrial training
Machinery production industries
Metal product industries
Metals industry
Neural networks
Neurons
Time frequency analysis
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ISBN9781424449941
1424449944
ISSN2156-7379
DOI10.1109/ICIECS.2009.5363667

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Summary:According to the mapping relationship between the common symptoms of fault in the asynchronous motor and fault mode, this paper established asynchronous motor fault diagnosis model by using the wavelet neural network (WNN). The model adopts the conjugate gradient descent algorithm, which is optimized by the momentum and adaptive learning rate. The initialization of parameters of the WNN is also analyzed in this paper. The final simulation results verified that, compared with conventional wavelet neural network and BP network, this model significantly reduces the training time and is valid for motor fault diagnosis.
ISBN:9781424449941
1424449944
ISSN:2156-7379
DOI:10.1109/ICIECS.2009.5363667