Rolling Bearing Fault Diagnostic Method Based on VMD-AR Model and Random Forest Classifier
Joint Authors
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-11, 11 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-09-20
Country of Publication
Egypt
No. of Pages
11
Main Subjects
Abstract EN
Targeting the nonstationary and non-Gaussian characteristics of vibration signal from fault rolling bearing, this paper proposes a fault feature extraction method based on variational mode decomposition (VMD) and autoregressive (AR) model parameters.
Firstly, VMD is applied to decompose vibration signals and a series of stationary component signals can be obtained.
Secondly, AR model is established for each component mode.
Thirdly, the parameters and remnant of AR model served as fault characteristic vectors.
Finally, a novel random forest (RF) classifier is put forward for pattern recognition in the field of rolling bearing fault diagnosis.
The validity and superiority of proposed method are verified by an experimental dataset.
Analysis results show that this method based on VMD-AR model can extract fault features accurately and RF classifier has been proved to outperform comparative classifiers.
American Psychological Association (APA)
Han, Te& Jiang, D. X.. 2016. Rolling Bearing Fault Diagnostic Method Based on VMD-AR Model and Random Forest Classifier. Shock and Vibration،Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1119267
Modern Language Association (MLA)
Han, Te& Jiang, D. X.. Rolling Bearing Fault Diagnostic Method Based on VMD-AR Model and Random Forest Classifier. Shock and Vibration No. 2016 (2016), pp.1-11.
https://search.emarefa.net/detail/BIM-1119267
American Medical Association (AMA)
Han, Te& Jiang, D. X.. Rolling Bearing Fault Diagnostic Method Based on VMD-AR Model and Random Forest Classifier. Shock and Vibration. 2016. Vol. 2016, no. 2016, pp.1-11.
https://search.emarefa.net/detail/BIM-1119267
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1119267