Application of Minimum Entropy Deconvolution in the Rolling Element Bearing Fault Diagnosis

When rolling element bearing occurred incipient fault,its vibration signal from bearing seat is usually very weak,and the hidden impulse components may be masked by machine noise or background noise,making it difficult to extract the fault features by frequency spectrum or envelope spectrum analysis...

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Main Authors: Leng Junfa, Yang Xin, Jing Shuangxi
Format: Article
Language:zho
Published: Editorial Office of Journal of Mechanical Transmission 2015-01-01
Series:Jixie chuandong
Online Access:http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2015.08.045
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author Leng Junfa
Yang Xin
Jing Shuangxi
author_facet Leng Junfa
Yang Xin
Jing Shuangxi
author_sort Leng Junfa
collection DOAJ
description When rolling element bearing occurred incipient fault,its vibration signal from bearing seat is usually very weak,and the hidden impulse components may be masked by machine noise or background noise,making it difficult to extract the fault features by frequency spectrum or envelope spectrum analysis. Through the method of minimum entropy deconvolution( MED),the signal to noise ratio can be improved,and the impulse components from fault signal can be highlighted. The MED method is applied to corresponding deconvolution filtering for three typical early fault signals from rolling element,outer race,and inner race. Then,through the amplitude spectrum and envelope demodulation analysis,the corresponding typical fault features of rolling element bearing are successfully extracted. The effectiveness and advantage of MED method in incipient fault diagnosis of rolling element bearing is verified by the application results.
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institution Kabale University
issn 1004-2539
language zho
publishDate 2015-01-01
publisher Editorial Office of Journal of Mechanical Transmission
record_format Article
series Jixie chuandong
spelling doaj-art-6406a02459cf4a0d966bafba0642233d2025-01-10T14:05:24ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392015-01-013918919229918556Application of Minimum Entropy Deconvolution in the Rolling Element Bearing Fault DiagnosisLeng JunfaYang XinJing ShuangxiWhen rolling element bearing occurred incipient fault,its vibration signal from bearing seat is usually very weak,and the hidden impulse components may be masked by machine noise or background noise,making it difficult to extract the fault features by frequency spectrum or envelope spectrum analysis. Through the method of minimum entropy deconvolution( MED),the signal to noise ratio can be improved,and the impulse components from fault signal can be highlighted. The MED method is applied to corresponding deconvolution filtering for three typical early fault signals from rolling element,outer race,and inner race. Then,through the amplitude spectrum and envelope demodulation analysis,the corresponding typical fault features of rolling element bearing are successfully extracted. The effectiveness and advantage of MED method in incipient fault diagnosis of rolling element bearing is verified by the application results.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2015.08.045
spellingShingle Leng Junfa
Yang Xin
Jing Shuangxi
Application of Minimum Entropy Deconvolution in the Rolling Element Bearing Fault Diagnosis
Jixie chuandong
title Application of Minimum Entropy Deconvolution in the Rolling Element Bearing Fault Diagnosis
title_full Application of Minimum Entropy Deconvolution in the Rolling Element Bearing Fault Diagnosis
title_fullStr Application of Minimum Entropy Deconvolution in the Rolling Element Bearing Fault Diagnosis
title_full_unstemmed Application of Minimum Entropy Deconvolution in the Rolling Element Bearing Fault Diagnosis
title_short Application of Minimum Entropy Deconvolution in the Rolling Element Bearing Fault Diagnosis
title_sort application of minimum entropy deconvolution in the rolling element bearing fault diagnosis
url http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2015.08.045
work_keys_str_mv AT lengjunfa applicationofminimumentropydeconvolutionintherollingelementbearingfaultdiagnosis
AT yangxin applicationofminimumentropydeconvolutionintherollingelementbearingfaultdiagnosis
AT jingshuangxi applicationofminimumentropydeconvolutionintherollingelementbearingfaultdiagnosis