Fault diagnosis method of rotating machinery based on stacked denoising autoencoder

Based on the deficiency in the traditional fault diagnosis method of rotating machinery, i.e. shallow learning is usually used to characterize complex mapping relationship between vibration signals and the rotor system, a deep neural network (DNN) based on stacked denoising autoencoder (SDAE) is pro...

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Bibliographic Details
Published inJournal of intelligent & fuzzy systems Vol. 34; no. 6; pp. 3443 - 3449
Main Authors Chen, Zhouliang, Li, Zhinong
Format Journal Article
LanguageEnglish
Published Amsterdam IOS Press BV 01.01.2018
Subjects
Artificial neural networks
Fault diagnosis
Feature extraction
Neural networks
Noise reduction
Principal components analysis
Rotating machinery
Rotation
Vibration
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Summary:Based on the deficiency in the traditional fault diagnosis method of rotating machinery, i.e. shallow learning is usually used to characterize complex mapping relationship between vibration signals and the rotor system, a deep neural network (DNN) based on stacked denoising autoencoder (SDAE) is proposed. The proposed method has been successfully applied to the fault diagnosis of rotating machinery. In the proposed method, the frequency domain information of vibration signal is used as input signal, and the deep neural network is obtained by layer-by-layer feature extraction from denoising autoencoder (DAE). Then the dropout method is used to adjust the network parameters, and reduces the over-fitting phenomenon. In additional, the principal component analysis is used to extract fault features. The experiment result shows that the proposed method is very effective, and can effectively extract the hidden features in the vibration signal of rotating machinery.
ISSN:1064-1246
1875-8967
DOI:10.3233/JIFS-169524