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The Fault Diagnosis of Rolling Bearing Based on Ensemble Empirical Mode Decomposition and Random Forest
Joint Authors
Qin, Xiwen
Li, Qiaoling
Dong, Xiaogang
Lv, Siqi
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-08-20
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
Accurate diagnosis of rolling bearing fault on the normal operation of machinery and equipment has a very important significance.
A method combining Ensemble Empirical Mode Decomposition (EEMD) and Random Forest (RF) is proposed.
Firstly, the original signal is decomposed into several intrinsic mode functions (IMFs) by EEMD, and the effective IMFs are selected.
Then their energy entropy is calculated as the feature.
Finally, the classification is performed by RF.
In addition, the wavelet method is also used in the proposed process, the same as EEMD.
The results of the comparison show that the EEMD method is more accurate than the wavelet method.
American Psychological Association (APA)
Qin, Xiwen& Li, Qiaoling& Dong, Xiaogang& Lv, Siqi. 2017. The Fault Diagnosis of Rolling Bearing Based on Ensemble Empirical Mode Decomposition and Random Forest. Shock and Vibration،Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1204152
Modern Language Association (MLA)
Qin, Xiwen…[et al.]. The Fault Diagnosis of Rolling Bearing Based on Ensemble Empirical Mode Decomposition and Random Forest. Shock and Vibration No. 2017 (2017), pp.1-9.
https://search.emarefa.net/detail/BIM-1204152
American Medical Association (AMA)
Qin, Xiwen& Li, Qiaoling& Dong, Xiaogang& Lv, Siqi. The Fault Diagnosis of Rolling Bearing Based on Ensemble Empirical Mode Decomposition and Random Forest. Shock and Vibration. 2017. Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1204152
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1204152