An Improved Method of EWT and Its Application in Rolling Bearings Fault Diagnosis

When the vibration signals of the rolling bearings contain strong interference noise, the spectrum division of the vibration signals is seriously disturbed by the noise. The traditional empirical wavelet transform (EWT) decomposes signals into a large number of components, and it is difficult to sel...

Full description

Saved in:
Bibliographic Details
Main Authors: Zhicheng Qiao, Yongqiang Liu, Yingying Liao
Format: Article
Language:English
Published: Wiley 2020-01-01
Series:Shock and Vibration
Online Access:http://dx.doi.org/10.1155/2020/4973941
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1832568123811692544
author Zhicheng Qiao
Yongqiang Liu
Yingying Liao
author_facet Zhicheng Qiao
Yongqiang Liu
Yingying Liao
author_sort Zhicheng Qiao
collection DOAJ
description When the vibration signals of the rolling bearings contain strong interference noise, the spectrum division of the vibration signals is seriously disturbed by the noise. The traditional empirical wavelet transform (EWT) decomposes signals into a large number of components, and it is difficult to select suitable components that contain fault information. In order to address the problems above, in this paper, we proposed the improved empirical wavelet transform (IEWT) method. The simulation experiment proved that IEWT can solve the problem of a large number of EWT components and separate the impact component effectively which contains bearing fault information from noise. The IEWT method is combined with the support vector machine (SVM) to diagnosis the fault of the rolling bearings. The permutation entropy (PE) is used to construct feature vectors for its strong induction ability of dynamic changes of nonstationary and nonlinear signals. The crucial parameter penalty factor C and kernel parameter g of SVM are optimized by quantum genetic algorithm (QGA). Compared with traditional EWT and variational mode decomposition (VMD) methods, the effectiveness and advantages of this method are demonstrated in this study. The classification prediction ability of SVM is also better than that of K-nearest neighbor (KNN) and extreme learning machine (ELM).
format Article
id doaj-art-0ac62fb9b2dc42e78c23f73381fe8d20
institution Kabale University
issn 1070-9622
1875-9203
language English
publishDate 2020-01-01
publisher Wiley
record_format Article
series Shock and Vibration
spelling doaj-art-0ac62fb9b2dc42e78c23f73381fe8d202025-02-03T00:59:42ZengWileyShock and Vibration1070-96221875-92032020-01-01202010.1155/2020/49739414973941An Improved Method of EWT and Its Application in Rolling Bearings Fault DiagnosisZhicheng Qiao0Yongqiang Liu1Yingying Liao2School of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, ChinaSchool of Mechanical Engineering, Shijiazhuang Tiedao University, Shijiazhuang 050043, ChinaState Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang 050043, ChinaWhen the vibration signals of the rolling bearings contain strong interference noise, the spectrum division of the vibration signals is seriously disturbed by the noise. The traditional empirical wavelet transform (EWT) decomposes signals into a large number of components, and it is difficult to select suitable components that contain fault information. In order to address the problems above, in this paper, we proposed the improved empirical wavelet transform (IEWT) method. The simulation experiment proved that IEWT can solve the problem of a large number of EWT components and separate the impact component effectively which contains bearing fault information from noise. The IEWT method is combined with the support vector machine (SVM) to diagnosis the fault of the rolling bearings. The permutation entropy (PE) is used to construct feature vectors for its strong induction ability of dynamic changes of nonstationary and nonlinear signals. The crucial parameter penalty factor C and kernel parameter g of SVM are optimized by quantum genetic algorithm (QGA). Compared with traditional EWT and variational mode decomposition (VMD) methods, the effectiveness and advantages of this method are demonstrated in this study. The classification prediction ability of SVM is also better than that of K-nearest neighbor (KNN) and extreme learning machine (ELM).http://dx.doi.org/10.1155/2020/4973941
spellingShingle Zhicheng Qiao
Yongqiang Liu
Yingying Liao
An Improved Method of EWT and Its Application in Rolling Bearings Fault Diagnosis
Shock and Vibration
title An Improved Method of EWT and Its Application in Rolling Bearings Fault Diagnosis
title_full An Improved Method of EWT and Its Application in Rolling Bearings Fault Diagnosis
title_fullStr An Improved Method of EWT and Its Application in Rolling Bearings Fault Diagnosis
title_full_unstemmed An Improved Method of EWT and Its Application in Rolling Bearings Fault Diagnosis
title_short An Improved Method of EWT and Its Application in Rolling Bearings Fault Diagnosis
title_sort improved method of ewt and its application in rolling bearings fault diagnosis
url http://dx.doi.org/10.1155/2020/4973941
work_keys_str_mv AT zhichengqiao animprovedmethodofewtanditsapplicationinrollingbearingsfaultdiagnosis
AT yongqiangliu animprovedmethodofewtanditsapplicationinrollingbearingsfaultdiagnosis
AT yingyingliao animprovedmethodofewtanditsapplicationinrollingbearingsfaultdiagnosis
AT zhichengqiao improvedmethodofewtanditsapplicationinrollingbearingsfaultdiagnosis
AT yongqiangliu improvedmethodofewtanditsapplicationinrollingbearingsfaultdiagnosis
AT yingyingliao improvedmethodofewtanditsapplicationinrollingbearingsfaultdiagnosis