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Train Wheelset Bearing Multifault Impulsive Component Separation Using Hierarchical Shift-Invariant Dictionary Learning
Joint Authors
Lin, Jianhui
Ding, Jianming
Zhang, Zhao-heng
Source
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-14, 14 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-09-11
Country of Publication
Egypt
No. of Pages
14
Main Subjects
Abstract EN
A wheelset bearing is a crucial energy transmission element in high-speed trains.
Any parts of the wheelset bearing that have faults may endanger the safety of the railway service.
Therefore, it is important to monitor the running condition of a wheelset bearing.
The multifault on a wheelset bearing is very common, and these impulsive components generated by different types of faults may interact with each other, which increases the difficulty of entirely identifying those faults.
To solve the multifault problem, this paper proposed a hierarchical shift-invariant K-means singular value decomposition (H-SI-K-SVD) to hierarchically separate those multifault impulsive components based on their fault power levels.
Each of the separated impulse signals contains only one fault impulse, and the fault information could be highlighted both in time domain and frequency domain.
In addition, the sparsity of envelope spectrum (SES) is introduced as an indicator to adaptively tune a key parameter in this method.
The effectiveness of the proposed method is verified by both simulation and experimental signals.
Compared with ensemble empirical model decomposition (EEMD), the proposed method exhibits better performance in separating the multifault impulsive components and detecting the faults of a wheelset bearing.
American Psychological Association (APA)
Zhang, Zhao-heng& Ding, Jianming& Lin, Jianhui. 2019. Train Wheelset Bearing Multifault Impulsive Component Separation Using Hierarchical Shift-Invariant Dictionary Learning. Shock and Vibration،Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1211344
Modern Language Association (MLA)
Zhang, Zhao-heng…[et al.]. Train Wheelset Bearing Multifault Impulsive Component Separation Using Hierarchical Shift-Invariant Dictionary Learning. Shock and Vibration No. 2019 (2019), pp.1-14.
https://search.emarefa.net/detail/BIM-1211344
American Medical Association (AMA)
Zhang, Zhao-heng& Ding, Jianming& Lin, Jianhui. Train Wheelset Bearing Multifault Impulsive Component Separation Using Hierarchical Shift-Invariant Dictionary Learning. Shock and Vibration. 2019. Vol. 2019, no. 2019, pp.1-14.
https://search.emarefa.net/detail/BIM-1211344
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1211344