A Hybrid Prognostics Approach for Estimating Remaining Useful Life of Rolling Element Bearings

被引：1363

作者：

Wang, Biao ^{[1
]}

Lei, Yaguo ^{[1
]}

Li, Naipeng ^{[1
]}

Li, Ningbo ^{[1
]}

机构：

[1] Xi An Jiao Tong Univ, Educ Minist Modern Design & Rotor Bearing Syst, Key Lab, Xian 710049, Peoples R China

来源：

IEEE TRANSACTIONS ON RELIABILITY | 2020年 / 69卷 / 01期

基金：

中国国家自然科学基金;

关键词：

Degradation; Predictive models; Data models; Kernel; Rolling bearings; Support vector machines; Adaptation models; Bearing degradation; prognostics; relevance vector machine; remaining useful life estimation; vibration monitoring; FEATURE-EXTRACTION; PREDICTION; DEGRADATION;

D O I：

10.1109/TR.2018.2882682

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

Remaining useful life (RUL) prediction of rolling element bearings plays a pivotal role in reducing costly unplanned maintenance and increasing the reliability, availability, and safety of machines. This paper proposes a hybrid prognostics approach for RUL prediction of rolling element bearings. First, degradation data of bearings are sparsely represented using relevance vector machine regressions with different kernel parameters. Then, exponential degradation models coupled with the Frechet distance are employed to estimate the RUL adaptively. The proposed approach is evaluated using the vibration data from accelerated degradation tests of rolling element bearings and the public PRONOSTIA bearing datasets. Experimental results demonstrate the effectiveness of the proposed approach in improving the accuracy and convergence of RUL prediction of rolling element bearings.

引用

页码：401 / 412

页数：12