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Published in: Wireless Personal Communications 2/2018

03-01-2018

Bearing Fault Diagnosis Using Multiclass Self-Adaptive Support Vector Classifiers Based on CEEMD–SVD

Authors: Zhipeng Wang, Limin Jia, Yong Qin

Published in: Wireless Personal Communications | Issue 2/2018

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Abstract

Bearing fault diagnosis under variable conditions has become a research hotspot recently. To solve this problem, this paper presents a new classifier: multiclass self-adaptive support vector classifier (MSa-SVC). Firstly, self-adaptive SVC is created by combination of SVC and information geometry. Then, several binary Sa-SVCs are constructed as a multiclass classifier for fault diagnosis. The proposed MSa-SVC, in conjunction with complementary ensemble empirical mode decomposition (CEEMD) and singular value decomposition (SVD) is utilized for bearing fault diagnosis: (1) each signal is processed into singular features by CEEMD–SVD. (2) MSa-SVC is used for fault clustering under variable conditions. Finally, the proposed method was applied on bearing fault diagnosis in practice. The results show that this method provides an efficient approach for bearing fault diagnosis under variable conditions.

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Metadata
Title
Bearing Fault Diagnosis Using Multiclass Self-Adaptive Support Vector Classifiers Based on CEEMD–SVD
Authors
Zhipeng Wang
Limin Jia
Yong Qin
Publication date
03-01-2018
Publisher
Springer US
Published in
Wireless Personal Communications / Issue 2/2018
Print ISSN: 0929-6212
Electronic ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-017-5226-8

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