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- Title
G-KSVD Dictionary and Its Applications in Sparse Representation of Rolling Bearing Fault Signals.
- Authors
MENG Zong; GAO Wenqing; PAN Zuozhou; ZHANG Guangya; FAN Fenje
- Abstract
For the lack of atomic coherence analysis and the lack of optimal atomic selection in the learning dictionary, a G-KSVD dictionary learning methods was proposed based on effective singular components herein. The method composed the following steps. First, the ACFPER was proposed based on the coherence of the signals. The singular component was screened to achieve the signal demising and the updating of the dictionary atom, and the solution of the confident were realized by using a component containing more fault information. Thus, the purpose of enhancing the impact components m the signals was achieved. Then the algorithm reduced the feedback layers in order to cut down the time cost. Last, the validity and repeatability of the proposed method were verified by using the simulation signal's and the actual bearing signals. The results show that the G-KSVD algorithm has good demising effectiveness in the identity interval, furthermore the time cost is low.
- Subjects
ROLLER bearings; ALGORITHMS; STATISTICAL correlation; PSYCHOLOGICAL feedback
- Publication
China Mechanical Engineering, 2021, Vol 32, Issue 15, p1776
- ISSN
1004-132X
- Publication type
Article
- DOI
10.3969/j.issn.1004-132X.2021.15.002