Fault diagnosis method study in roller bearing based on wavelet transform and stacked auto-encoder

Considering the nonlinear and non-stationary characteristics of fault vibration signal in the roller bearing system, an intelligent fault diagnosis model based on wavelet transform and stacked auto-encoder is proposed. This paper firstly uses the combination of digital wavelet frame (DWF) and nonlin...

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Bibliographic Details
Published inThe 27th Chinese Control and Decision Conference (2015 CCDC) pp. 4608 - 4613
Main Authors Tan Junbo, Lu Weining, An Juneng, Wan Xueqian
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.05.2015
Subjects
deep learning
Fault diagnosis
Feature extraction
Neural networks
Noise reduction
roller bearing
stacked auto-encoder
Vibrations
wavelet transform
Wavelet transforms
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Summary:Considering the nonlinear and non-stationary characteristics of fault vibration signal in the roller bearing system, an intelligent fault diagnosis model based on wavelet transform and stacked auto-encoder is proposed. This paper firstly uses the combination of digital wavelet frame (DWF) and nonlinear soft threshold method to de-noise fault vibration signal. Then stacked auto-encoder is taken to extract the fault signal feature, which is regarded as the input of BP network classifier. The output results of BP network classifier represent fault categories. In addition, neural network ensemble method is also adopted to greatly improve the recognition rate of fault diagnosis.
ISSN:1948-9439
DOI:10.1109/CCDC.2015.7162738