Bearing Degradation Process Prediction Based on the Support Vector Machine and Markov Model
Joint Authors
Yin, Shirong
Tang, Baoping
Chen, Lili
Luo, Tianhong
Dong, Shaojiang
Source
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-15, 15 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-03-05
Country of Publication
Egypt
No. of Pages
15
Main Subjects
Abstract EN
Predicting the degradation process of bearings before they reach the failure threshold is extremely important in industry.
This paper proposed a novel method based on the support vector machine (SVM) and the Markov model to achieve this goal.
Firstly, the features are extracted by time and time-frequency domain methods.
However, the extracted original features are still with high dimensional and include superfluous information, and the nonlinear multifeatures fusion technique LTSA is used to merge the features and reduces the dimension.
Then, based on the extracted features, the SVM model is used to predict the bearings degradation process, and the CAO method is used to determine the embedding dimension of the SVM model.
After the bearing degradation process is predicted by SVM model, the Markov model is used to improve the prediction accuracy.
The proposed method was validated by two bearing run-to-failure experiments, and the results proved the effectiveness of the methodology.
American Psychological Association (APA)
Dong, Shaojiang& Yin, Shirong& Tang, Baoping& Chen, Lili& Luo, Tianhong. 2014. Bearing Degradation Process Prediction Based on the Support Vector Machine and Markov Model. Shock and Vibration،Vol. 2014, no. 2014, pp.1-15.
https://search.emarefa.net/detail/BIM-1047985
Modern Language Association (MLA)
Dong, Shaojiang…[et al.]. Bearing Degradation Process Prediction Based on the Support Vector Machine and Markov Model. Shock and Vibration No. 2014 (2014), pp.1-15.
https://search.emarefa.net/detail/BIM-1047985
American Medical Association (AMA)
Dong, Shaojiang& Yin, Shirong& Tang, Baoping& Chen, Lili& Luo, Tianhong. Bearing Degradation Process Prediction Based on the Support Vector Machine and Markov Model. Shock and Vibration. 2014. Vol. 2014, no. 2014, pp.1-15.
https://search.emarefa.net/detail/BIM-1047985
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1047985