Facility health maintenance through SVR-driven degradation prediction

被引：8

作者：

Cao, Xiangang ^{[1
]}

Jiang, Pingyu ^{[1
]}

Zhou, Guanghui ^{[1
]}

机构：

[1] Xi An Jiao Tong Univ, Sch Mech Engn, State Key Lab Mfg Syst Engn, Xian 710049, Shaanxi, Peoples R China

来源：

INTERNATIONAL JOURNAL OF MATERIALS & PRODUCT TECHNOLOGY | 2008年 / 33卷 / 1-2期

关键词：

health monitoring and maintenance; facility synthetic failure probability model; logistic regression; support vector regression; degradation trend;

D O I：

10.1504/IJMPT.2008.019781

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In order to realise the health monitoring and maintenance of complex facilities with multiple degradation parameters, a facility synthetic failure probability model to map between inputs and probability of failure is established through adopting the logistic regression to synthesise each degradation parameter. Then, a SVR-driven degradation trend prediction and estimate of Remaining Useful Life (RUL) method is put forward. Last, based on Monte-Carlo method, a multi-parameters equipment emulator according with Weibull distribution is established to test the model. The results show that these methods are practicable.

引用

页码：185 / 193

页数：9

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