DocumentCode :
605990
Title :
Application of fast incremental LLE to bearing fault feature dimension reduction
Author :
Li Chengliang ; Wang Zhongsheng ; Jiang Hongkai
Author_Institution :
Sch. of Aeronaut., Northwestern Polytech. Univ., Xi´an, China
fYear :
2012
fDate :
23-25 Oct. 2012
Firstpage :
423
Lastpage :
426
Abstract :
Focus on incremental local linear embedding (LLE) operation efficiency problem, this paper proposes a fast incremental LLE algorithm. Firstly, we describe briefly the basic principle of LLE algorithm. Secondly, based on the incremental learning principle, the new samples are added, the global coordinates of affect samples are recomputed. Thirdly, the low dimensional embedding coordinates of the incremental samples are formulated by the updated global coordinate matrix and low dimensional embedding coordinates of the given samples, then, using Rayleigh-Ritz accelerated iterative algorithm calculate the global coordinate update. Experiment results show that the proposed algorithm can fastly establish the low dimensional feature for new samples.
Keywords :
Rayleigh-Ritz methods; fault diagnosis; iterative methods; learning (artificial intelligence); machine bearings; matrix algebra; mechanical engineering computing; Rayleigh-Ritz accelerated iterative algorithm; bearing fault feature dimension reduction; global coordinate matrix; global coordinate update; incremental LLE algorithm; incremental learning principle; incremental local linear embedding operation efficiency problem; low-dimensional embedding coordinates; Incremental LLE; Rayleigh-Ritz; dimension reduction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information Science and Service Science and Data Mining (ISSDM), 2012 6th International Conference on New Trends in
Conference_Location :
Taipei
Print_ISBN :
978-1-4673-0876-2
Type :
conf
Filename :
6528670
Link To Document :
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