Gearbox Composite Fault Diagnosis based on MRSVD Energy Characteristic and KFCM

A composite gearbox fault diagnosis method based on multi-resolution singular value decomposition (MRSVD) energy characteristic and fuzzy kernel clustering (KFCM) is proposed for the complex fault signal components of the gearbox and the difficulty in identifying fault characteristic. Firstly, the v...

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Main Authors: Haiying Liang, Xin Xu, Hongxia Pan, Zhimin Fu
Format: Article
Language:zho
Published: Editorial Office of Journal of Mechanical Transmission 2019-09-01
Series:Jixie chuandong
Subjects:
Online Access:http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2019.09.022
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author Haiying Liang
Xin Xu
Hongxia Pan
Zhimin Fu
author_facet Haiying Liang
Xin Xu
Hongxia Pan
Zhimin Fu
author_sort Haiying Liang
collection DOAJ
description A composite gearbox fault diagnosis method based on multi-resolution singular value decomposition (MRSVD) energy characteristic and fuzzy kernel clustering (KFCM) is proposed for the complex fault signal components of the gearbox and the difficulty in identifying fault characteristic. Firstly, the vibration signal of the gearbox under different working conditions is acquired to obtain a similar signal and five detail signals by performing MRSVD decomposition. Then, the energy characteristic of the six component signals are extracted and normalized to obtain the relative value of the energy. Finally, KFCM is used for fault diagnosis. The experimental results show that the MRSVD energy characteristic extraction method can effectively extract the composite fault characteristic of the gearbox, and KFCM can accurately diagnose the gearbox composite fault.
format Article
id doaj-art-6a613714cbfa4fd69150ca74651d1502
institution Kabale University
issn 1004-2539
language zho
publishDate 2019-09-01
publisher Editorial Office of Journal of Mechanical Transmission
record_format Article
series Jixie chuandong
spelling doaj-art-6a613714cbfa4fd69150ca74651d15022025-01-10T13:57:49ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392019-09-014312813230641777Gearbox Composite Fault Diagnosis based on MRSVD Energy Characteristic and KFCMHaiying LiangXin XuHongxia PanZhimin FuA composite gearbox fault diagnosis method based on multi-resolution singular value decomposition (MRSVD) energy characteristic and fuzzy kernel clustering (KFCM) is proposed for the complex fault signal components of the gearbox and the difficulty in identifying fault characteristic. Firstly, the vibration signal of the gearbox under different working conditions is acquired to obtain a similar signal and five detail signals by performing MRSVD decomposition. Then, the energy characteristic of the six component signals are extracted and normalized to obtain the relative value of the energy. Finally, KFCM is used for fault diagnosis. The experimental results show that the MRSVD energy characteristic extraction method can effectively extract the composite fault characteristic of the gearbox, and KFCM can accurately diagnose the gearbox composite fault.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2019.09.022GearboxMRSVDEnergy characteristicKFCMFault diagnosis
spellingShingle Haiying Liang
Xin Xu
Hongxia Pan
Zhimin Fu
Gearbox Composite Fault Diagnosis based on MRSVD Energy Characteristic and KFCM
Jixie chuandong
Gearbox
MRSVD
Energy characteristic
KFCM
Fault diagnosis
title Gearbox Composite Fault Diagnosis based on MRSVD Energy Characteristic and KFCM
title_full Gearbox Composite Fault Diagnosis based on MRSVD Energy Characteristic and KFCM
title_fullStr Gearbox Composite Fault Diagnosis based on MRSVD Energy Characteristic and KFCM
title_full_unstemmed Gearbox Composite Fault Diagnosis based on MRSVD Energy Characteristic and KFCM
title_short Gearbox Composite Fault Diagnosis based on MRSVD Energy Characteristic and KFCM
title_sort gearbox composite fault diagnosis based on mrsvd energy characteristic and kfcm
topic Gearbox
MRSVD
Energy characteristic
KFCM
Fault diagnosis
url http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2019.09.022
work_keys_str_mv AT haiyingliang gearboxcompositefaultdiagnosisbasedonmrsvdenergycharacteristicandkfcm
AT xinxu gearboxcompositefaultdiagnosisbasedonmrsvdenergycharacteristicandkfcm
AT hongxiapan gearboxcompositefaultdiagnosisbasedonmrsvdenergycharacteristicandkfcm
AT zhiminfu gearboxcompositefaultdiagnosisbasedonmrsvdenergycharacteristicandkfcm