Deep Domain Adaptation Model for Bearing Fault Diagnosis with Domain Alignment and Discriminative Feature Learning
Joint Authors
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-03-20
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
Deep learning techniques have been widely used to achieve promising results for fault diagnosis.
In many real-world fault diagnosis applications, labeled training data (source domain) and unlabeled test data (target domain) have different distributions due to the frequent changes of working conditions, leading to performance degradation.
This study proposes an end-to-end unsupervised domain adaptation bearing fault diagnosis model that combines domain alignment and discriminative feature learning on the basis of a 1D convolutional neural network.
Joint training with classification loss, center-based discriminative loss, and correlation alignment loss between the two domains can adapt learned representations in the source domain for application to the target domain.
Such joint training can also guarantee domain-invariant features with good intraclass compactness and interclass separability.
Meanwhile, the extracted features can efficiently improve the cross-domain testing performance.
Experimental results on the Case Western Reserve University bearing datasets confirm the superiority of the proposed method over many existing methods.
American Psychological Association (APA)
An, Jing& Ai, Ping& Liu, Dakun. 2020. Deep Domain Adaptation Model for Bearing Fault Diagnosis with Domain Alignment and Discriminative Feature Learning. Shock and Vibration،Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1209941
Modern Language Association (MLA)
An, Jing…[et al.]. Deep Domain Adaptation Model for Bearing Fault Diagnosis with Domain Alignment and Discriminative Feature Learning. Shock and Vibration No. 2020 (2020), pp.1-14.
https://search.emarefa.net/detail/BIM-1209941
American Medical Association (AMA)
An, Jing& Ai, Ping& Liu, Dakun. Deep Domain Adaptation Model for Bearing Fault Diagnosis with Domain Alignment and Discriminative Feature Learning. Shock and Vibration. 2020. Vol. 2020, no. 2020, pp.1-14.
https://search.emarefa.net/detail/BIM-1209941
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1209941