Rolling Bearing Fault Diagnosis Based on Stacked Autoencoder Network with Dynamic Learning Rate
Joint Authors
Tang, Wei
Pan, Hong
Xu, Jin-Jun
Binama, Maxime
Source
Advances in Materials Science and Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-12-28
Country of Publication
Egypt
No. of Pages
12
Abstract EN
Fault diagnosis is of great significance for ensuring the safety and reliable operation of rolling bearing in industries.
Stack autoencoder (SAE) networks have been widely applied in this field.
However, the model parameters such as learning rate are always fixed, which have an adverse effect on the convergence speed and accuracy of fault classification.
Thus, this paper proposes a dynamic learning rate adjustment approach for the stacked autoencoder network.
First, the input data is normalized and enhanced.
Second, the structure of the SAE network is selected.
According to the positive and negative value of the training error gradient, a learning rate reducing strategy is designed in order to be consistent with the current operation of the network.
Finally, the fault diagnosis models with different learning rate adjustment are conducted in order to validate the better performance of the proposed approach.
In addition, the influence of quantities of labeled sample data on the process of backpropagation is analyzed.
The results show that the proposed method can effectively increase the convergence speed and improve classification accuracy.
Moreover, it can reduce the labeled sample size and make the network more stable under the same classification accuracy.
American Psychological Association (APA)
Pan, Hong& Tang, Wei& Xu, Jin-Jun& Binama, Maxime. 2020. Rolling Bearing Fault Diagnosis Based on Stacked Autoencoder Network with Dynamic Learning Rate. Advances in Materials Science and Engineering،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1128871
Modern Language Association (MLA)
Pan, Hong…[et al.]. Rolling Bearing Fault Diagnosis Based on Stacked Autoencoder Network with Dynamic Learning Rate. Advances in Materials Science and Engineering No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1128871
American Medical Association (AMA)
Pan, Hong& Tang, Wei& Xu, Jin-Jun& Binama, Maxime. Rolling Bearing Fault Diagnosis Based on Stacked Autoencoder Network with Dynamic Learning Rate. Advances in Materials Science and Engineering. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1128871
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1128871