Review of Remaining Useful Life Prediction Using Support Vector Machine for Engineering Assets

被引：0

作者：

Zhang, Longlong ^{[1
]}

Liu, Zhiliang ^{[1
]}

Luo, Dashuang ^{[1
]}

Li, Jing ^{[1
]}

Huang, Hong-Zhong ^{[1
]}

机构：

[1] Univ Elect Sci & Technol China, Sch Mech Elect & Ind Engn, Chengdu 611731, Sichuan, Peoples R China

来源：

PROCEEDINGS OF 2013 INTERNATIONAL CONFERENCE ON QUALITY, RELIABILITY, RISK, MAINTENANCE, AND SAFETY ENGINEERING (QR2MSE), VOLS I-IV | 2013年

关键词：

prognostics and health management; degradation model; remaining useful life; support vector machine;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Remaining useful life (RUL) is important to manage life circles of machineries and reduce maintenance cost. Support vector machine (SVM) is a promising algorithm for RUL prediction because of its advantages to deal with small size of training sets and multi-dimensional data. Recently, many methods of RUL prediction using SVM have been proposed. In this paper, a review over 60 references within the last 10 years on this topic is conducted, which includes introduction of the improvement algorithms and applications of using SVM to predict RUL and possible problems to be solved in future work.

引用

页码：1793 / 1799

页数：7

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