Rolling Bearing Degradation State Identification Based on LPP Optimized by GAReport as inadecuate

Rolling Bearing Degradation State Identification Based on LPP Optimized by GA - Download this document for free, or read online. Document in PDF available to download.

International Journal of Rotating Machinery - Volume 2016 2016, Article ID 9281098, 10 pages -

Research ArticleMechanical Engineering College, No. 97, Heping West Road, Shijiazhuang 050003, China

Received 5 May 2016; Accepted 12 July 2016

Academic Editor: Hyeong Joon Ahn

Copyright © 2016 He Yu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


In view of the problem that the actual degradation status of rolling bearing has a poor distinguishing characteristic and strong fuzziness, a rolling bearing degradation state identification method based on multidomain feature fusion and dimension reduction of manifold learning combined with GG clustering is proposed. Firstly, the rolling bearing all-life data is preprocessed by local characteristic-scale decomposition LCD and six typical features including relative energy spectrum entropy LREE, relative singular spectrum entropy LRSE, two-element multiscale entropy TMSE, standard deviation STD, RMS, and root-square amplitude XR are extracted and compose the original multidomain feature set. And then, locally preserving projection LPP is utilized to reduce dimension of original fusion feature set and genetic algorithm is applied to optimize the process of feature fusion. Finally, fuzzy recognition of rolling bearing degradation state is carried out by GG clustering and the principle of maximum membership degree and excellent performance of the proposed method is validated by comparing the recognition accuracy of LPP and GA-LPP.

Author: He Yu, Hong-ru Li, Zai-ke Tian, and Wei-guo Wang



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