Rolling bearing health prognosis using a modified health index based hierarchical gated recurrent unit network

被引：106

作者：

Li, Xingqiu ^{[1
]}

Jiang, Hongkai ^{[1
]}

Xiong, Xiong ^{[1
]}

Shao, Haidong ^{[1
]}

机构：

[1] Northwestern Polytech Univ, Sch Aeronaut, Xian 710072, Shaanxi, Peoples R China

来源：

MECHANISM AND MACHINE THEORY | 2019年 / 133卷

基金：

中国国家自然科学基金;

关键词：

Rolling bearing; Health prognosis; Modified health index; Hierarchical gated recurrent unit network; Exponentially weighted moving average; REMAINING-USEFUL-LIFE; PERFORMANCE DEGRADATION ASSESSMENT; CONVOLUTIONAL NEURAL-NETWORK; HIDDEN MARKOV-MODELS; DEEP BELIEF NETWORK; FAULT-DIAGNOSIS; ROTATING MACHINERY; FUZZY ENTROPY; PREDICTION;

D O I：

10.1016/j.mechmachtheory.2018.11.005

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

Rolling bearing health prognosis is helpful to improve the operation efficiency and security of rotating machinery. In this paper, a modified health index based hierarchical gated recurrent unit network is proposed for rolling bearing health prognosis. Firstly, in order to effectively depict the degradation process, a modified health index is designed based on kernel principle component analysis (KPCA) and exponentially weighted moving average (EWMA). Then, in order to capture the high nonlinear characteristics and assess the health condition, a hierarchical gated recurrent unit network is constructed by stacking multiple hidden layers. Finally, the proposed method is applied for rolling bearing health prognosis with the experimental bearing data, and the results confirm that it outperforms other existing methods. (C) 2018 Elsevier Ltd. All rights reserved.

引用

页码：229 / 249

页数：21

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