Bearing fault diagnosis method based on stacked autoencoder and softmax regression

As bearings are the most common components of mechanical structure, it will be helpful to research bearing fault and diagnose the fault as early as possible in case of suffering greater losses. This paper proposes a deep neural network algorithm framework for bearing fault diagnosis based on stacked...

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Bibliographic Details
Published in2015 34th Chinese Control Conference (CCC) pp. 6331 - 6335
Main Authors Tao, Siqin, Zhang, Tao, Yang, Jun, Wang, Xueqian, Lu, Weining
Format Conference Proceeding Journal Article
LanguageEnglish
Published Technical Committee on Control Theory, Chinese Association of Automation 01.07.2015
Subjects
Accuracy
Algorithms
Bearing
Classification
Computer simulation
Conferences
Cost function
Fault diagnosis
Neural networks
Noise
Regression
Robustness
Softmax Regression
Stacked Autoencoder
Training
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Summary:As bearings are the most common components of mechanical structure, it will be helpful to research bearing fault and diagnose the fault as early as possible in case of suffering greater losses. This paper proposes a deep neural network algorithm framework for bearing fault diagnosis based on stacked autoencoder and softmax regression. The simulation results verify the feasibility of the algorithm and show the excellent classification performance. In addition, this deep neural network represents strong robustness and eliminates the impact of noise remarkably. Last but not least, an integrated deep neural network method consisting of ten different structure parameter networks is proposed and it has better generalization capability.
Bibliography:ObjectType-Article-2
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SourceType-Conference Papers & Proceedings-2
ISSN:1934-1768
DOI:10.1109/ChiCC.2015.7260634