期刊
MEASUREMENT
卷 46, 期 9, 页码 3143-3152出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2013.06.038
关键词
Degradation process prediction; Principal component analysis; Least squares support vector machine; Bearing
资金
- Natural Science Foundation Project of CQ
- [CSTC 2013jcyjA70012]
- [CSTC 2009AC6077]
Bearing degradation process prediction is extremely important in industry. This paper proposed a new method to achieve bearing degradation prediction based on principal component analysis (PCA) and optimized LS-SVM method. Firstly, the time domain, frequency domain, time-frequency domain features extraction methods are employed to extract the original features from the mass vibration signals. However, the extracted original features still with high dimensional and include superfluous information, the multi-features fusion technique PCA is used to merge the original features and reduce the dimension, the typical sensitive features are extracted. Then, based on the extracted features, the LS-SVM model is constructed and trained for bearing degradation process prediction. The pseudo nearest neighbor point method is used to determine the input number of the model. The particle swarm optimization (PSO) is used to selected the LS-SVM parameters. An accelerated bearing run-to-failure experiment was carried out, the results proved the effectiveness of the methodology. (C) 2013 Elsevier Ltd. All rights reserved.
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