Train Wheelset Bearing Multifault Impulsive Component Separation Using Hierarchical Shift-Invariant Dictionary Learning
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...
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| Format: | Article |
| Language: | English |
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Wiley
2019-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2019/5697137 |
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| _version_ | 1850214771882721280 |
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| author | Zhao-heng Zhang Jian-ming Ding Jian-hui Lin |
| author_facet | Zhao-heng Zhang Jian-ming Ding Jian-hui Lin |
| author_sort | Zhao-heng Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-86505f154a9c4ff0831186c0357694cb |
| institution | OA Journals |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-86505f154a9c4ff0831186c0357694cb2025-08-20T02:08:47ZengWileyShock and Vibration1070-96221875-92032019-01-01201910.1155/2019/56971375697137Train Wheelset Bearing Multifault Impulsive Component Separation Using Hierarchical Shift-Invariant Dictionary LearningZhao-heng Zhang0Jian-ming Ding1Jian-hui Lin2State Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, ChinaState Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, ChinaState Key Laboratory of Traction Power, Southwest Jiaotong University, Chengdu 610031, ChinaA 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.http://dx.doi.org/10.1155/2019/5697137 |
| spellingShingle | Zhao-heng Zhang Jian-ming Ding Jian-hui Lin Train Wheelset Bearing Multifault Impulsive Component Separation Using Hierarchical Shift-Invariant Dictionary Learning Shock and Vibration |
| title | Train Wheelset Bearing Multifault Impulsive Component Separation Using Hierarchical Shift-Invariant Dictionary Learning |
| title_full | Train Wheelset Bearing Multifault Impulsive Component Separation Using Hierarchical Shift-Invariant Dictionary Learning |
| title_fullStr | Train Wheelset Bearing Multifault Impulsive Component Separation Using Hierarchical Shift-Invariant Dictionary Learning |
| title_full_unstemmed | Train Wheelset Bearing Multifault Impulsive Component Separation Using Hierarchical Shift-Invariant Dictionary Learning |
| title_short | Train Wheelset Bearing Multifault Impulsive Component Separation Using Hierarchical Shift-Invariant Dictionary Learning |
| title_sort | train wheelset bearing multifault impulsive component separation using hierarchical shift invariant dictionary learning |
| url | http://dx.doi.org/10.1155/2019/5697137 |
| work_keys_str_mv | AT zhaohengzhang trainwheelsetbearingmultifaultimpulsivecomponentseparationusinghierarchicalshiftinvariantdictionarylearning AT jianmingding trainwheelsetbearingmultifaultimpulsivecomponentseparationusinghierarchicalshiftinvariantdictionarylearning AT jianhuilin trainwheelsetbearingmultifaultimpulsivecomponentseparationusinghierarchicalshiftinvariantdictionarylearning |