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KPCA and AE Based Local-Global Feature Extraction Method for Vibration Signals of Rotating Machinery
Joint Authors
Xiao, Zhihuai
Malik, O. P.
Hu, Xiao
Liu, Dong
Tang, Yongjun
Xia, Xiangchen
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-17, 17 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-06-29
Country of Publication
Egypt
No. of Pages
17
Main Subjects
Abstract EN
Feature extraction plays a key role in fault diagnosis of rotating machinery.
Many methods reported in the literature are based on masses of labeled data and need much prior knowledge to select the most discriminating features or establish a complex deep-learning model.
To solve the dilemma, a novel feature extraction method based on kernel principal component analysis (KPCA) and an autoencoder (AE), namely, SFS-KPCA-AE, is presented in this paper to automatically extract the most discriminative features from the frequency spectrum of vibration signals.
First, fast Fourier transform is calculated on the entire vibration signal to get the frequency spectrum.
Next, the spectrum is divided into several segments.
Then, local-global feature extraction is performed by applying KPCA to these segments.
Finally, an AE is employed to obtain the low-dimensional representations of the high-dimensional global feature.
The proposed feature extraction method combined with a classifier achieves fault diagnosis for rotating machinery.
A rotor dataset and a bearing dataset are utilized to validate the performance of the proposed method.
Experimental results demonstrate that the proposed method achieved satisfactory performance in feature extraction when training samples or motor load changed.
By comparing with other methods, the superiority of the proposed SFS-KPCA-AE is verified.
American Psychological Association (APA)
Hu, Xiao& Xiao, Zhihuai& Liu, Dong& Tang, Yongjun& Malik, O. P.& Xia, Xiangchen. 2020. KPCA and AE Based Local-Global Feature Extraction Method for Vibration Signals of Rotating Machinery. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1196285
Modern Language Association (MLA)
Hu, Xiao…[et al.]. KPCA and AE Based Local-Global Feature Extraction Method for Vibration Signals of Rotating Machinery. Mathematical Problems in Engineering No. 2020 (2020), pp.1-17.
https://search.emarefa.net/detail/BIM-1196285
American Medical Association (AMA)
Hu, Xiao& Xiao, Zhihuai& Liu, Dong& Tang, Yongjun& Malik, O. P.& Xia, Xiangchen. KPCA and AE Based Local-Global Feature Extraction Method for Vibration Signals of Rotating Machinery. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-17.
https://search.emarefa.net/detail/BIM-1196285
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1196285