Intelligent fault diagnosis method of rolling bearing based on stacked denoising autoencoder and convolutional neural network

Purpose The purpose of this study is to analyze the intelligent fault diagnosis method of rolling bearing. Design/methodology/approach The vibration signal data of rolling bearing has long time series and strong noise interference, which brings great difficulties for the accurate diagnosis of bearin...

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Veröffentlicht in:Industrial lubrication and tribology Jg. 72; H. 7; S. 947 - 953
Hauptverfasser: Che, Changchang, Wang, Huawei, Ni, Xiaomei, Fu, Qiang
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Bradford Emerald Publishing Limited 17.09.2020
Emerald Group Publishing Limited
Schlagworte:
Artificial neural networks
Back propagation
Classification
Convolution
Fault diagnosis
Feature extraction
Interference
Methods
Model accuracy
Neural networks
Noise
Noise levels
Noise reduction
Propagation
Roller bearings
Signal processing
Time series
Vibration
Fault diagnosis
Stacked denoising autoencoder (SDAE)
Convolutional neural network (CNN)
Rolling bearing
Vibration signal
ISSN:0036-8792, 1758-5775
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Zusammenfassung:Purpose The purpose of this study is to analyze the intelligent fault diagnosis method of rolling bearing. Design/methodology/approach The vibration signal data of rolling bearing has long time series and strong noise interference, which brings great difficulties for the accurate diagnosis of bearing faults. To solve those problems, an intelligent fault diagnosis model based on stacked denoising autoencoder (SDAE) and convolutional neural network (CNN) is proposed in this paper. The SDAE is used to process the time series data with multiple dimensions and noise interference. Then the dimension-reduced samples can be put into CNN model, and the fault classification results can be obtained by convolution and pooling operations of hidden layers in CNN. Findings The effectiveness of the proposed method is validated through experimental verification and comparative experimental analysis. The results demonstrate that the proposed model can achieve an average classification accuracy of 96.5% under three noise levels, which is 3-13% higher than the traditional models and single deep-learning models. Originality/value The combined SDAE–CNN model proposed in this paper can denoise and reduce dimensions of raw vibration signal data, and extract the in-depth features in image samples of rolling bearing. Consequently, the proposed model has more accurate fault diagnosis results for the rolling bearing vibration signal data with long time series and noise interference. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2019-0496/
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ISSN:0036-8792
1758-5775
DOI:10.1108/ILT-11-2019-0496