An Adaptive Remaining Life Prediction for Rolling Element Bearings

被引：2

作者：

Zhang S. ^{[1
]}

Zhang Y. ^{[1
]}

Zhu J. ^{[1
]}

机构：

[1] Naval University of Engineering Power Engineering Marine Engineering, Wuhan

来源：

Journal of Failure Analysis and Prevention | 2014年 / 15卷 / 01期

关键词：

An adaptive prediction model; Generative topographic mapping; K-means clustering algorithm; Remaining life prediction; Rolling bearings;

D O I：

10.1007/s11668-014-9906-3

中图分类号：

学科分类号：

摘要：

In order to select the effective health index and build reasonably the prediction model for prognostics, a new approach is proposed. The generative topographic mapping-based negative likelihood probability is used as the health index, and K-means clustering algorithm is employed for state division. The adaptive prediction model based on Markov model and least mean square algorithm is built by the historical data and the online monitoring data. According to the given threshold, the remaining life can be captured. Based on experimental verification, the results indicate that the selected health index is able to effectively reflect the condition of rolling bearings and the proposed model shows high prediction accuracy in comparison to the common one. © 2014, ASM International.

引用

页码：82 / 89

页数：7

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