A Novel Cuckoo Search Optimized Deep Auto-Encoder Network-Based Fault Diagnosis Method for Rolling Bearing
Joint Authors
Zheng, Jinde
Pan, Haiyang
Luo, Jin
Tong, Jinyu
Zhang, Qing
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-09-24
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
To enhance the performance of deep auto-encoder (AE) under complex working conditions, a novel deep auto-encoder network method for rolling bearing fault diagnosis is proposed in this paper.
First, multiscale analysis is adopted to extract the multiscale features from the raw vibration signals of rolling bearing.
Second, the sparse penalty term and contractive penalty term are used simultaneously to regularize the loss function of auto-encoder to enhance the feature learning ability of networks.
Finally, the cuckoo search algorithm (CS) is used to find the optimal hyperparameters automatically.
The proposed method is applied to the experimental data analysis.
The results indicate that the proposed method could more effectively distinguish fault categories and severities of rolling bearings under different working conditions than other methods.
American Psychological Association (APA)
Tong, Jinyu& Luo, Jin& Pan, Haiyang& Zheng, Jinde& Zhang, Qing. 2020. A Novel Cuckoo Search Optimized Deep Auto-Encoder Network-Based Fault Diagnosis Method for Rolling Bearing. Shock and Vibration،Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1213621
Modern Language Association (MLA)
Tong, Jinyu…[et al.]. A Novel Cuckoo Search Optimized Deep Auto-Encoder Network-Based Fault Diagnosis Method for Rolling Bearing. Shock and Vibration No. 2020 (2020), pp.1-12.
https://search.emarefa.net/detail/BIM-1213621
American Medical Association (AMA)
Tong, Jinyu& Luo, Jin& Pan, Haiyang& Zheng, Jinde& Zhang, Qing. A Novel Cuckoo Search Optimized Deep Auto-Encoder Network-Based Fault Diagnosis Method for Rolling Bearing. Shock and Vibration. 2020. Vol. 2020, no. 2020, pp.1-12.
https://search.emarefa.net/detail/BIM-1213621
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1213621