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The Fault Diagnosis of Rolling Bearing Based on Variational Mode Decomposition and Iterative Random Forest
Joint Authors
Qin, Xiwen
Dong, Xiaogang
Guo, Jiajing
Guo, Yu
Source
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-02-12
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Rolling bearing is a critical part of machinery, whose failure will lead to considerable losses and disastrous consequences.
Aiming at the research of rotating mechanical bearing data, a fault identification method based on Variational Mode Decomposition (VMD) and Iterative Random Forest (iRF) classifier is proposed.
Furthermore, EMD and EEMD are used to decompose the data.
At the same time, three mainstream classifiers were selected as the benchmark model.
The results show that the proposed model has the highest recognition accuracy.
American Psychological Association (APA)
Qin, Xiwen& Guo, Jiajing& Dong, Xiaogang& Guo, Yu. 2020. The Fault Diagnosis of Rolling Bearing Based on Variational Mode Decomposition and Iterative Random Forest. Shock and Vibration،Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1209657
Modern Language Association (MLA)
Qin, Xiwen…[et al.]. The Fault Diagnosis of Rolling Bearing Based on Variational Mode Decomposition and Iterative Random Forest. Shock and Vibration No. 2020 (2020), pp.1-11.
https://search.emarefa.net/detail/BIM-1209657
American Medical Association (AMA)
Qin, Xiwen& Guo, Jiajing& Dong, Xiaogang& Guo, Yu. The Fault Diagnosis of Rolling Bearing Based on Variational Mode Decomposition and Iterative Random Forest. Shock and Vibration. 2020. Vol. 2020, no. 2020, pp.1-11.
https://search.emarefa.net/detail/BIM-1209657
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1209657