![](/images/graphics-bg.png)
Rolling Bearing Degradation State Identification Based on LPP Optimized by GA
Joint Authors
Yu, He
Li, Hong-ru
Tian, Zai-ke
Wang, Wei-guo
Source
International Journal of Rotating Machinery
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-08-11
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Yu, He& Li, Hong-ru& Tian, Zai-ke& Wang, Wei-guo. 2016. Rolling Bearing Degradation State Identification Based on LPP Optimized by GA. International Journal of Rotating Machinery،Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1107052
Modern Language Association (MLA)
Yu, He…[et al.]. Rolling Bearing Degradation State Identification Based on LPP Optimized by GA. International Journal of Rotating Machinery No. 2016 (2016), pp.1-10.
https://search.emarefa.net/detail/BIM-1107052
American Medical Association (AMA)
Yu, He& Li, Hong-ru& Tian, Zai-ke& Wang, Wei-guo. Rolling Bearing Degradation State Identification Based on LPP Optimized by GA. International Journal of Rotating Machinery. 2016. Vol. 2016, no. 2016, pp.1-10.
https://search.emarefa.net/detail/BIM-1107052
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1107052