SVM Prediction of Performance Degradation of Rolling Bearings with Fusion of KPCA and Information Granulation

doi:10.16182/j.issn1004731x.joss.201806043

Abstract

Abstract: To effectively predict the performance degradation index and its fluctuation ranges of the rolling bearing, a prediction method based on kernel principal component analysis algorithm and fuzzy information granulation using support vector machine is proposed. The kernel principal component analysis is utilized to preprocess the data to acquire the main feature vector, construct T² and SPE statistics, and to analyze its trend. The statistical information is used as the performance degradation index. Theory of fuzzy information granulation is used to granulate the performance degradation index and extract the useful information. The granulated data are put to the support vector machine for regression prediction. The experiment results show the prediction method can track the change tendency of the performance degradation index of rolling bearing and the fluctuation range of its index effectively.

Key words: kernel principal component analysis, fuzzy information granulation, support vector machines, rolling bearing

CLC Number:

TP391.9

Xu Jiya, Wang Yan, Yan Dahu, Ji Zhicheng. SVM Prediction of Performance Degradation of Rolling Bearings with Fusion of KPCA and Information Granulation[J]. Journal of System Simulation, 2018, 30(6): 2345-2354.

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