Railway Wheel Flat Detection Based on Improved Empirical Mode Decomposition
This study explores the capacity of the improved empirical mode decomposition (EMD) in railway wheel flat detection. Aiming at the mode mixing problem of EMD, an EMD energy conservation theory and an intrinsic mode function (IMF) superposition theory are presented and derived, respectively. Based on...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-01-01
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| Series: | Shock and Vibration |
| Online Access: | http://dx.doi.org/10.1155/2016/4879283 |
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| _version_ | 1849308112925753344 |
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| author | Yifan Li Jianxin Liu Yan Wang |
| author_facet | Yifan Li Jianxin Liu Yan Wang |
| author_sort | Yifan Li |
| collection | DOAJ |
| description | This study explores the capacity of the improved empirical mode decomposition (EMD) in railway wheel flat detection. Aiming at the mode mixing problem of EMD, an EMD energy conservation theory and an intrinsic mode function (IMF) superposition theory are presented and derived, respectively. Based on the above two theories, an improved EMD method is further proposed. The advantage of the improved EMD is evaluated by a simulated vibration signal. Then this method is applied to study the axle box vibration response caused by wheel flats, considering the influence of both track irregularity and vehicle running speed on diagnosis results. Finally, the effectiveness of the proposed method is verified by a test rig experiment. Research results demonstrate that the improved EMD can inhibit mode mixing phenomenon and extract the wheel fault characteristic effectively. |
| format | Article |
| id | doaj-art-13d9cd056b7d4e62b5dd7ef8d052b855 |
| institution | Kabale University |
| issn | 1070-9622 1875-9203 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Shock and Vibration |
| spelling | doaj-art-13d9cd056b7d4e62b5dd7ef8d052b8552025-08-20T03:54:33ZengWileyShock and Vibration1070-96221875-92032016-01-01201610.1155/2016/48792834879283Railway Wheel Flat Detection Based on Improved Empirical Mode DecompositionYifan Li0Jianxin Liu1Yan Wang2Department of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, ChinaTraction Power State Key Laboratory, Southwest Jiaotong University, Chengdu 610031, ChinaDepartment of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, ChinaThis study explores the capacity of the improved empirical mode decomposition (EMD) in railway wheel flat detection. Aiming at the mode mixing problem of EMD, an EMD energy conservation theory and an intrinsic mode function (IMF) superposition theory are presented and derived, respectively. Based on the above two theories, an improved EMD method is further proposed. The advantage of the improved EMD is evaluated by a simulated vibration signal. Then this method is applied to study the axle box vibration response caused by wheel flats, considering the influence of both track irregularity and vehicle running speed on diagnosis results. Finally, the effectiveness of the proposed method is verified by a test rig experiment. Research results demonstrate that the improved EMD can inhibit mode mixing phenomenon and extract the wheel fault characteristic effectively.http://dx.doi.org/10.1155/2016/4879283 |
| spellingShingle | Yifan Li Jianxin Liu Yan Wang Railway Wheel Flat Detection Based on Improved Empirical Mode Decomposition Shock and Vibration |
| title | Railway Wheel Flat Detection Based on Improved Empirical Mode Decomposition |
| title_full | Railway Wheel Flat Detection Based on Improved Empirical Mode Decomposition |
| title_fullStr | Railway Wheel Flat Detection Based on Improved Empirical Mode Decomposition |
| title_full_unstemmed | Railway Wheel Flat Detection Based on Improved Empirical Mode Decomposition |
| title_short | Railway Wheel Flat Detection Based on Improved Empirical Mode Decomposition |
| title_sort | railway wheel flat detection based on improved empirical mode decomposition |
| url | http://dx.doi.org/10.1155/2016/4879283 |
| work_keys_str_mv | AT yifanli railwaywheelflatdetectionbasedonimprovedempiricalmodedecomposition AT jianxinliu railwaywheelflatdetectionbasedonimprovedempiricalmodedecomposition AT yanwang railwaywheelflatdetectionbasedonimprovedempiricalmodedecomposition |