REVIEW ARTICLE
Application of EEMD Sample Entropy and Grey Relation Degree in Gearbox Fault Identification
Wenbin Zhang*, Libin Yu, Yanping Su, Jie Min, Yasong Pu
Article Information
Identifiers and Pagination:
Year: 2014Volume: 8
First Page: 402
Last Page: 408
Publisher Id: TOMEJ-8-402
DOI: 10.2174/1874155X01408010402
Article History:
Received Date: 10/09/2014Revision Received Date: 05/11/2014
Acceptance Date: 05/11/2014
Electronic publication date: 09/12/2014
Collection year: 2014
open-access license: This is an open access article distributed under the terms of the Creative Commons Attribution 4.0 International Public License (CC-BY 4.0), a copy of which is available at: (https://creativecommons.org/licenses/by/4.0/legalcode). This license permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
In this paper, a new gearbox fault identification method was proposed based on mathematical morphological filter, ensemble empirical mode decomposition (EEMD), sample entropy and grey relation degree. Firstly, the sampled data was de-noised by mathematical morphological filter. Secondly, the de-noised signal was decomposed into a finite number of stationary intrinsic mode functions (IMFs) by EEMD method. Thirdly, some IMFs containing the most dominant fault information were calculated by the sample entropy for four gearbox conditions. Finally, since the grey relation degree has good classified capacity for small sample pattern identification, the grey relation degree between the symptom set and standard fault set was calculated as the identification evidence for fault diagnosis. The practical results show that this method is quite effective in gearbox fault diagnosis. It’s suitable for on-line monitoring and fault diagnosis of gearbox.
