Manifold Learning with Self-Organizing Mapping for Feature Extraction of Nonlinear Faults in Rotating Machinery
Joint Authors
Liang, Lin
Liu, Fei
Li, Maolin
Xu, Guanghua
Source
Mathematical Problems in Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-10-07
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
A new method for extracting the low-dimensional feature automatically with self-organization mapping manifold is proposed for the detection of rotating mechanical nonlinear faults (such as rubbing, pedestal looseness).
Under the phase space reconstructed by single vibration signal, the self-organization mapping (SOM) with expectation maximization iteration algorithm is used to divide the local neighborhoods adaptively without manual intervention.
After that, the local tangent space alignment algorithm is adopted to compress the high-dimensional phase space into low-dimensional feature space.
The proposed method takes advantages of the manifold learning in low-dimensional feature extraction and adaptive neighborhood construction of SOM and can extract intrinsic fault features of interest in two dimensional projection space.
To evaluate the performance of the proposed method, the Lorenz system was simulated and rotation machinery with nonlinear faults was obtained for test purposes.
Compared with the holospectrum approaches, the results reveal that the proposed method is superior in identifying faults and effective for rotating machinery condition monitoring.
American Psychological Association (APA)
Liang, Lin& Liu, Fei& Li, Maolin& Xu, Guanghua. 2015. Manifold Learning with Self-Organizing Mapping for Feature Extraction of Nonlinear Faults in Rotating Machinery. Mathematical Problems in Engineering،Vol. 2015, no. 2015, pp.1-11.
https://search.emarefa.net/detail/BIM-1074947
Modern Language Association (MLA)
Liang, Lin…[et al.]. Manifold Learning with Self-Organizing Mapping for Feature Extraction of Nonlinear Faults in Rotating Machinery. Mathematical Problems in Engineering No. 2015 (2015), pp.1-11.
https://search.emarefa.net/detail/BIM-1074947
American Medical Association (AMA)
Liang, Lin& Liu, Fei& Li, Maolin& Xu, Guanghua. Manifold Learning with Self-Organizing Mapping for Feature Extraction of Nonlinear Faults in Rotating Machinery. Mathematical Problems in Engineering. 2015. Vol. 2015, no. 2015, pp.1-11.
https://search.emarefa.net/detail/BIM-1074947
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1074947