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: Yifan Li, Jianxin Liu, Yan Wang
Format: Article
Language:English
Published: Wiley 2016-01-01
Series:Shock and Vibration
Online Access:http://dx.doi.org/10.1155/2016/4879283
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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