Assessment Method for Rolling Bearing Performance Degradation Using TESPAR and GMM
Long Zhang, Wen-yi Huang*, Guo-liang Xiong, Ji-hui Zhou
Identifiers and Pagination:Year: 2014
First Page: 503
Last Page: 508
Publisher Id: TOMEJ-8-503
Article History:Received Date: 10/09/2014
Revision Received Date: 05/11/2014
Acceptance Date: 05/11/2014
Electronic publication date: 24/12/2014
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.
Rolling bearing performance degradation assessment has been receiving much attention for which itscrucial role to realize CBM(condition-based maintenance).This paper proposed a novel bearing performance degradation method based on TESPAR(Time Encoded Signal Processing and Recognition)and GMM(Gauss Mixture Model). TESPAR is used to extracted features which constitute A-matrix. GMM is utilized to approximate the density distribution of singular values decomposed by A-matrix. TENLLP(Time-Encoded Negative Log Likelihood Probability) serves as a fault severity which can display the similarity of the singular values between normal samples and fault samples as quantificational. Results of its application to bearing fatigue test show that this performance degradation assessment can detect the incipient rolling bearing fault and be sensitive to the change of fault.