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Deep Transfer Learning-Based Fault Diagnosis for Gearbox under Complex Working Conditions
Joint Authors
Wan, Zitong
Yang, Rui
Huang, Mengjie
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-11-16
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
In the large amount of available data, information insensitive to faults in historical data interferes in gear fault feature extraction.
Furthermore, as most of the fault diagnosis models are learned from offline data collected under single/fixed working condition only, this may cause unsatisfactory performance for complex working conditions (including multiple and unknown working conditions) if not properly dealt with.
This paper proposes a transfer learning-based fault diagnosis method of gear faults to reduce the negative effects of the abovementioned problems.
In the proposed method, a cohesion evaluation method is applied to select sensitive features to the task with a transfer learning-based sparse autoencoder to transfer the knowledge learnt under single working condition to complex working conditions.
The experimental results on wind turbine drivetrain diagnostics simulator show that the proposed method is effective in complex working conditions and the achieved results are better than those of traditional algorithms.
American Psychological Association (APA)
Wan, Zitong& Yang, Rui& Huang, Mengjie. 2020. Deep Transfer Learning-Based Fault Diagnosis for Gearbox under Complex Working Conditions. Shock and Vibration،Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1213151
Modern Language Association (MLA)
Wan, Zitong…[et al.]. Deep Transfer Learning-Based Fault Diagnosis for Gearbox under Complex Working Conditions. Shock and Vibration No. 2020 (2020), pp.1-13.
https://search.emarefa.net/detail/BIM-1213151
American Medical Association (AMA)
Wan, Zitong& Yang, Rui& Huang, Mengjie. Deep Transfer Learning-Based Fault Diagnosis for Gearbox under Complex Working Conditions. Shock and Vibration. 2020. Vol. 2020, no. 2020, pp.1-13.
https://search.emarefa.net/detail/BIM-1213151
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1213151