Abstract:A new prediction method is proposed based on manifold learning and least squares support vector machine to describe the rolling bearing degradation trend. Time-domain features and features based on information entropy were extracted to construct high-dimensional characteristic sets. The locality preserving projection algorithm was used for dimensionality reduction in order to eliminate the problem of redundancy between each indicators. The characteristic features were input to the least squares support vector machine to train and construct a model, so as to accomplish the trend prediction. The rolling bearing run-to-failure tests are carried out to inspect the prediction model, and the results demonstrate the effectiveness and accurateness of the proposed method.
肖婷,汤宝平,秦毅,陈昌. 基于流形学习和最小二乘支持向量机的滚动轴承退化趋势预测 [J]. 振动与冲击, 2015, 34(9): 149-153.
XIAO Ting, TANG Bao-ping*,QIN Yi,CHEN Chang. Degradation Trend Prediction of Rolling Bearing Based on Manifold Learning and Least Squares Support Vector Machine. JOURNAL OF VIBRATION AND SHOCK, 2015, 34(9): 149-153.
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