Application of Gaussian Process Regression in Prediction of Bearing Health
Bearing is an important part of train traction motor and accurate evaluation of its subsequent health status is very important for train safe operation.Based on the bearing bench test and real vehicle data,principal component analysis(PCA)and Gaussian process regression(GPR)are applied to assese and predict bearing health status.Features are extracted from the experimental data,obtaining the characteristic data that can characterize the law of bearing degradation.PCA is used to reduce the dimension of extracted data and establish the bearing state assessment data,and GPR is applied to learn and predict the assessment data.Through the comparison of the predicted value and the true value,GPR can predict the health of bearing and maintain a high accuracy rate at a low sampling rate.
bearing condition predictionGaussian process regressionprincipal component analysistraction motor
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