Application of ELMD-MCKD in Rolling Bearing Fault Diagnosis

Aiming at the problem that the fault information of rolling bearing is weak and the characteristic frequency is difficult to be identified under strong noise environment,the method of fault diagnosis based on the ensemble local mean decomposition( ELMD) and the maximum correlated kurtosis deconvolut...

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Main Authors: He Yuanyuan, Zhang Chao, Zhu Tengfei
Format: Article
Language:zho
Published: Editorial Office of Journal of Mechanical Transmission 2018-01-01
Series:Jixie chuandong
Subjects:
Online Access:http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2018.05.033
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author He Yuanyuan
Zhang Chao
Zhu Tengfei
author_facet He Yuanyuan
Zhang Chao
Zhu Tengfei
author_sort He Yuanyuan
collection DOAJ
description Aiming at the problem that the fault information of rolling bearing is weak and the characteristic frequency is difficult to be identified under strong noise environment,the method of fault diagnosis based on the ensemble local mean decomposition( ELMD) and the maximum correlated kurtosis deconvolution( MCKD) is proposed,and it is used to handle the bearing fault vibration signal. Firstly,the original data is decomposed into a set of product functions( PF) by ELMD. Then,each PF component is subjected to noise reduction processing by MCKD. Finally,the PF component of each noise reduction is obtained by finding the envelope spectrum,so as to find the fault characteristic frequency of the bearing in the envelope. In order to verify the effectiveness of ELMD-MCKD in detecting faults,a series of bearing failure simulation experiments are carried out.The results show that the proposed method of ELMD-MCKD can improve the accuracy of bearing fault identification and can be used in fault diagnosis in practical application.
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institution Kabale University
issn 1004-2539
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publisher Editorial Office of Journal of Mechanical Transmission
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series Jixie chuandong
spelling doaj-art-ad5098a4ce264e82bf34ed83b44e5c6d2025-01-10T14:42:25ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392018-01-014216116629936764Application of ELMD-MCKD in Rolling Bearing Fault DiagnosisHe YuanyuanZhang ChaoZhu TengfeiAiming at the problem that the fault information of rolling bearing is weak and the characteristic frequency is difficult to be identified under strong noise environment,the method of fault diagnosis based on the ensemble local mean decomposition( ELMD) and the maximum correlated kurtosis deconvolution( MCKD) is proposed,and it is used to handle the bearing fault vibration signal. Firstly,the original data is decomposed into a set of product functions( PF) by ELMD. Then,each PF component is subjected to noise reduction processing by MCKD. Finally,the PF component of each noise reduction is obtained by finding the envelope spectrum,so as to find the fault characteristic frequency of the bearing in the envelope. In order to verify the effectiveness of ELMD-MCKD in detecting faults,a series of bearing failure simulation experiments are carried out.The results show that the proposed method of ELMD-MCKD can improve the accuracy of bearing fault identification and can be used in fault diagnosis in practical application.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2018.05.033ELMDMCKDProduct functionFault diagnosis
spellingShingle He Yuanyuan
Zhang Chao
Zhu Tengfei
Application of ELMD-MCKD in Rolling Bearing Fault Diagnosis
Jixie chuandong
ELMD
MCKD
Product function
Fault diagnosis
title Application of ELMD-MCKD in Rolling Bearing Fault Diagnosis
title_full Application of ELMD-MCKD in Rolling Bearing Fault Diagnosis
title_fullStr Application of ELMD-MCKD in Rolling Bearing Fault Diagnosis
title_full_unstemmed Application of ELMD-MCKD in Rolling Bearing Fault Diagnosis
title_short Application of ELMD-MCKD in Rolling Bearing Fault Diagnosis
title_sort application of elmd mckd in rolling bearing fault diagnosis
topic ELMD
MCKD
Product function
Fault diagnosis
url http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2018.05.033
work_keys_str_mv AT heyuanyuan applicationofelmdmckdinrollingbearingfaultdiagnosis
AT zhangchao applicationofelmdmckdinrollingbearingfaultdiagnosis
AT zhutengfei applicationofelmdmckdinrollingbearingfaultdiagnosis