Bearing Fault Diagnosis with Kernel Sparse Representation Classification Based on Adaptive Local Iterative Filtering-Enhanced Multiscale Entropy Features
Joint Authors
Liu, Ming
Zhang, Jinbao
Zhao, Yongqiang
Li, Xinglin
Source
Mathematical Problems in Engineering
Issue
Vol. 2019, Issue 2019 (31 Dec. 2019), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2019-06-04
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Abstract EN
To improve the bearings diagnosis accuracy considering multiple fault types with small samples, a new approach that combined adaptive local iterative filtering (ALIF), multiscale entropy features, and kernel sparse representation classification (KSRC) is put forward in this paper.
ALIF is used to adaptively decompose the nonlinear, nonstationary vibration signals into a sum of intrinsic mode functions (IMFs).
Multiple entropy features such as sample entropy, fuzzy entropy, and permutation entropy with multiscale are computed from the first three IMFs and a total of one hundred and eighty features are obtained.
After normalization, the features are employed to train and test the classifier KSRC, respectively.
Finally, the proposed approach is evaluated with two experimental tests.
One is concerned with different types of bearing faults from the centrifugal pump; and the other is from Case Western Reserve University (CWRU) considering 12 bearing fault states.
Experimental results have proved that the proposed approach is efficient for bearing fault diagnosis, and high accuracy will be obtained with high dimensional features through small samples.
American Psychological Association (APA)
Zhang, Jinbao& Zhao, Yongqiang& Li, Xinglin& Liu, Ming. 2019. Bearing Fault Diagnosis with Kernel Sparse Representation Classification Based on Adaptive Local Iterative Filtering-Enhanced Multiscale Entropy Features. Mathematical Problems in Engineering،Vol. 2019, no. 2019, pp.1-17.
https://search.emarefa.net/detail/BIM-1197229
Modern Language Association (MLA)
Zhang, Jinbao…[et al.]. Bearing Fault Diagnosis with Kernel Sparse Representation Classification Based on Adaptive Local Iterative Filtering-Enhanced Multiscale Entropy Features. Mathematical Problems in Engineering No. 2019 (2019), pp.1-17.
https://search.emarefa.net/detail/BIM-1197229
American Medical Association (AMA)
Zhang, Jinbao& Zhao, Yongqiang& Li, Xinglin& Liu, Ming. Bearing Fault Diagnosis with Kernel Sparse Representation Classification Based on Adaptive Local Iterative Filtering-Enhanced Multiscale Entropy Features. Mathematical Problems in Engineering. 2019. Vol. 2019, no. 2019, pp.1-17.
https://search.emarefa.net/detail/BIM-1197229
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1197229