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Shock and Vibration
Volume 2014, Article ID 717465, 15 pages
http://dx.doi.org/10.1155/2014/717465
Research Article

Bearing Degradation Process Prediction Based on the Support Vector Machine and Markov Model

1School of Mechatronics and Automotive Engineering, Chongqing Jiaotong University, Chongqing 400074, China
2The State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400030, China
3Key Laboratory of Road Construction Technology and Equipment, Ministry of Education, Chang’an University, Xi’an 710021, China

Received 15 March 2013; Accepted 5 August 2013; Published 5 March 2014

Academic Editor: Valder Steffen

Copyright © 2014 Shaojiang Dong et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

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.