Compound Fault Feature Extraction of Gearbox with Improved Local Mean Decomposition

In the case of strong noise, Ensemble local mean decomposition (ELMD) is proposed for the modal aliasing phenomenon of local mean decomposition(LMD). However, the white noise added in ELMD cannot be completely neutralized, which will result in the reconstruction error increases due to the Product fu...

Full description

Saved in:
Bibliographic Details
Main Authors: Huili Chai, Meitao Ye
Format: Article
Language:zho
Published: Editorial Office of Journal of Mechanical Transmission 2019-08-01
Series:Jixie chuandong
Subjects:
Online Access:http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2019.08.024
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1841548859311915008
author Huili Chai
Meitao Ye
author_facet Huili Chai
Meitao Ye
author_sort Huili Chai
collection DOAJ
description In the case of strong noise, Ensemble local mean decomposition (ELMD) is proposed for the modal aliasing phenomenon of local mean decomposition(LMD). However, the white noise added in ELMD cannot be completely neutralized, which will result in the reconstruction error increases due to the Product functions(PF)components to be affected by the added white noise. Therefore, a compound fault feature extraction method for gearbox based on PE-CELMD(Permutation Entropy-Complementary Ensemble local mean decomposition) is proposed. The idea is to optimize ELMD by adding pairwise white noise in combination with Permutation Entropy (PE) method based on ELMD. The method is applied to the simulated signal and the measured signal, and compared with LMD and CELMD, the results show that the PE-CELMD method is an effective compound fault feature extraction method.
format Article
id doaj-art-c0d30760c7cd41cc88a9d864fe2bd6aa
institution Kabale University
issn 1004-2539
language zho
publishDate 2019-08-01
publisher Editorial Office of Journal of Mechanical Transmission
record_format Article
series Jixie chuandong
spelling doaj-art-c0d30760c7cd41cc88a9d864fe2bd6aa2025-01-10T13:59:15ZzhoEditorial Office of Journal of Mechanical TransmissionJixie chuandong1004-25392019-08-014313013430643777Compound Fault Feature Extraction of Gearbox with Improved Local Mean DecompositionHuili ChaiMeitao YeIn the case of strong noise, Ensemble local mean decomposition (ELMD) is proposed for the modal aliasing phenomenon of local mean decomposition(LMD). However, the white noise added in ELMD cannot be completely neutralized, which will result in the reconstruction error increases due to the Product functions(PF)components to be affected by the added white noise. Therefore, a compound fault feature extraction method for gearbox based on PE-CELMD(Permutation Entropy-Complementary Ensemble local mean decomposition) is proposed. The idea is to optimize ELMD by adding pairwise white noise in combination with Permutation Entropy (PE) method based on ELMD. The method is applied to the simulated signal and the measured signal, and compared with LMD and CELMD, the results show that the PE-CELMD method is an effective compound fault feature extraction method.http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2019.08.024Local mean decompositionPermutation entropyCompound fault
spellingShingle Huili Chai
Meitao Ye
Compound Fault Feature Extraction of Gearbox with Improved Local Mean Decomposition
Jixie chuandong
Local mean decomposition
Permutation entropy
Compound fault
title Compound Fault Feature Extraction of Gearbox with Improved Local Mean Decomposition
title_full Compound Fault Feature Extraction of Gearbox with Improved Local Mean Decomposition
title_fullStr Compound Fault Feature Extraction of Gearbox with Improved Local Mean Decomposition
title_full_unstemmed Compound Fault Feature Extraction of Gearbox with Improved Local Mean Decomposition
title_short Compound Fault Feature Extraction of Gearbox with Improved Local Mean Decomposition
title_sort compound fault feature extraction of gearbox with improved local mean decomposition
topic Local mean decomposition
Permutation entropy
Compound fault
url http://www.jxcd.net.cn/thesisDetails#10.16578/j.issn.1004.2539.2019.08.024
work_keys_str_mv AT huilichai compoundfaultfeatureextractionofgearboxwithimprovedlocalmeandecomposition
AT meitaoye compoundfaultfeatureextractionofgearboxwithimprovedlocalmeandecomposition