Support vector machine based estimation of remaining useful life: current research status and future trends
Support vector machine based estimation of remaining useful life: current research status and future trends
复制标题
基于支持向量机的剩余使用寿命估计：研究现状与未来趋势
DOI：
10.1007/s12206-014-1222-z
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发表时间：
2015-01-01
影响因子：
1.6
通讯作者：
Liu, Zhiliang
中科院分区：
文献类型：
--
作者：
Huang, Hong-Zhong;Wang, Hai-Kun;Liu, Zhiliang
剩余使用寿命（RUL）的估计有助于管理机器的生命周期，降低维护成本。支持向量机（SVM）是一种很有前途的算法，用于估计RUL，因为它可以很容易地处理小的训练集和多维数据。许多基于支持向量机的方法已经被提出来预测一些关键部件的RUL。我们做了一个文献综述，在十年内基于SVM的RUL估计。文献综述分为两大类：改进的SVM算法及其在RUL估计中的应用。后者又可进一步分为两类：一是预测未来的条件状态，进而建立状态与RUL之间的关系;二是建立当前状态与RUL之间的直接关系。然而，支持向量机很少被用来跟踪退化过程，并建立一个准确的关系，当前的健康状况状态和RUL。在此基础上，本文指出，不断改进支持向量机的能力，为使用支持向量机进行规则语言预测提供新的思路将是未来的工作方向。
Estimation of remaining useful life (RUL) is helpful to manage life cycles of machines and to reduce maintenance cost. Support vector machine (SVM) is a promising algorithm for estimation of RUL because it can easily process small training sets and multi-dimensional data. Many SVM based methods have been proposed to predict RUL of some key components. We did a literature review related to SVM based RUL estimation within a decade. The references reviewed are classified into two categories: improved SVM algorithms and their applications to RUL estimation. The latter category can be further divided into two types: one, to predict the condition state in the future and then build a relationship between state and RUL; two, to establish a direct relationship between current state and RUL. However, SVM is seldom used to track the degradation process and build an accurate relationship between the current health condition state and RUL. Based on the above review and summary, this paper points out that the ability to continually improve SVM, and obtain a novel idea for RUL prediction using SVM will be future works.