Bearing Fault Diagnosis Based on Domain Adaptation Using Transferable Features under Different Working Conditions
Joint Authors
Wei, Li
Tong, Zhe
Zhang, Bo
Zhang, Meng
Source
Issue
Vol. 2018, Issue 2018 (31 Dec. 2018), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2018-06-28
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
Bearing failure is the most common failure mode in rotating machinery and can result in large financial losses or even casualties.
However, complex structures around bearing and actual variable working conditions can lead to large distribution difference of vibration signal between a training set and a test set, which causes the accuracy-dropping problem of fault diagnosis.
Thus, how to improve efficiently the performance of bearing fault diagnosis under different working conditions is always a primary challenge.
In this paper, a novel bearing fault diagnosis under different working conditions method is proposed based on domain adaptation using transferable features(DATF).
The datasets of normal bearing and faulty bearings are obtained through the fast Fourier transformation (FFT) of raw vibration signals under different motor speeds and load conditions.
Then we reduce marginal and conditional distributions simultaneously across domains based on maximum mean discrepancy (MMD) in feature space by refining pseudo test labels, which can be obtained by the nearest-neighbor (NN) classifier built on training data, and then a robust transferable feature representation for training and test domains is achieved after several iterations.
With the help of the NN classifier trained on transferable features, bearing fault categories are identified accurately in final.
Extensive experiment results show that the proposed method under different working conditions can identify the bearing faults accurately and outperforms obviously competitive approaches.
American Psychological Association (APA)
Tong, Zhe& Wei, Li& Zhang, Bo& Zhang, Meng. 2018. Bearing Fault Diagnosis Based on Domain Adaptation Using Transferable Features under Different Working Conditions. Shock and Vibration،Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1215394
Modern Language Association (MLA)
Tong, Zhe…[et al.]. Bearing Fault Diagnosis Based on Domain Adaptation Using Transferable Features under Different Working Conditions. Shock and Vibration No. 2018 (2018), pp.1-12.
https://search.emarefa.net/detail/BIM-1215394
American Medical Association (AMA)
Tong, Zhe& Wei, Li& Zhang, Bo& Zhang, Meng. Bearing Fault Diagnosis Based on Domain Adaptation Using Transferable Features under Different Working Conditions. Shock and Vibration. 2018. Vol. 2018, no. 2018, pp.1-12.
https://search.emarefa.net/detail/BIM-1215394
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1215394