关键词: 主成分分析, 极限学习机, 多变量极限学习机, 剩余寿命预测

Abstract:

A predictive model of the residual life for bearings based on PCA and MELM was proposed herein. To eliminate the redundancy and relevance within features, PCA was applied to fuse multiple characteristic indexes in both of time and frequency domains which could reflect the running states and degradation trends of bearings. Further, on the basis of univariate ELM, a new MELM model was constructed to predict the remaining life of bearings. Unlike traditional univariate ELM, the proposed method overcomes the problems of the simple structure and the lack of information and can improve the prediction precision of residual life for bearings effectively. The bearing run-to-failure tests were carried out to verify the prediction model, and the experimental results demonstrate that the proposed model based on PCA and MELM has the better prediction precision and stability than that of single ELM model or MELM model.

Key words: principle component analysis(PCA);extreme learning machine(ELM);multivariable extreme learning machine(MELM), residual life prediction