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Wheelset-Bearing Fault Detection Using Adaptive Convolution Sparse Representation
Joint Authors
Yin, Yanli
Ding, Jianming
Zhang, Zhao-heng
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-26, 26 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-11-14
Country of Publication
Egypt
No. of Pages
26
Main Subjects
Abstract EN
Wheelset bearings are crucial mechanical components of high-speed trains.
Wheelset-bearing fault detection is of great significance to ensure the safety of high-speed train service.
Convolution sparse representations (CSRs) provide an excellent framework for extracting impulse responses induced by bearing faults.
However, the performance of CSR on extracting impulse responses is fairly sensitive to inappropriate selection of method-related parameters, and a convolution model for representing the impulse responses has not been discussed.
In view of these two unsolved problems, a convolutional representation model of the impulse response series is developed.
A novel fault detection method, named adaptive CSR (ACSR), is then proposed based on combinations of CSR and methods for estimating three parameters related to CSR.
Finally, the effectiveness of the proposed ACSR method is validated via simulation, bench testing, and a real-life running test employing a high-speed train.
American Psychological Association (APA)
Ding, Jianming& Zhang, Zhao-heng& Yin, Yanli. 2019. Wheelset-Bearing Fault Detection Using Adaptive Convolution Sparse Representation. Shock and Vibration،Vol. 2019, no. 2019, pp.1-26.
https://search.emarefa.net/detail/BIM-1211494
Modern Language Association (MLA)
Ding, Jianming…[et al.]. Wheelset-Bearing Fault Detection Using Adaptive Convolution Sparse Representation. Shock and Vibration No. 2019 (2019), pp.1-26.
https://search.emarefa.net/detail/BIM-1211494
American Medical Association (AMA)
Ding, Jianming& Zhang, Zhao-heng& Yin, Yanli. Wheelset-Bearing Fault Detection Using Adaptive Convolution Sparse Representation. Shock and Vibration. 2019. Vol. 2019, no. 2019, pp.1-26.
https://search.emarefa.net/detail/BIM-1211494
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1211494