Avionics Remain Life Prediction Using Multiple Kernel LS-SVR

Fei Li; Ying Chen; Yangming Guo; Chenglie Du; Hao Wu; Congbao Ran

Avionics Remain Life Prediction Using Multiple Kernel LS-SVR

Fei Li, Ying Chen, Yangming Guo, Chenglie Du, Hao Wu, Congbao Ran

Research output: Contribution to journal › Article › peer-review

2 Scopus citations

Abstract

Avionics equipments are important parts of the aircraft system, and their failure probability is higher and higher, which will affect the performance of the whole system. A prediction model based on MKLS-SVR is proposed in this paper and used for remain life prediction with an avionic device. The simulation results show that the MKLS-SVR has a higher accuracy, comparing with the traditional model LS-SVR, and it is a practical and effective electronic equipment life prediction method.

Original language	English
Pages (from-to)	724-728
Number of pages	5
Journal	Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University
Volume	35
Issue number	4
State	Published - 1 Aug 2017

Keywords

Matlab
Root mean square error(RMSE)
Support vector machines(SVM)
Time series

Cite this

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title = "Avionics Remain Life Prediction Using Multiple Kernel LS-SVR",

abstract = "Avionics equipments are important parts of the aircraft system, and their failure probability is higher and higher, which will affect the performance of the whole system. A prediction model based on MKLS-SVR is proposed in this paper and used for remain life prediction with an avionic device. The simulation results show that the MKLS-SVR has a higher accuracy, comparing with the traditional model LS-SVR, and it is a practical and effective electronic equipment life prediction method.",

keywords = "Matlab, Root mean square error(RMSE), Support vector machines(SVM), Time series",

author = "Fei Li and Ying Chen and Yangming Guo and Chenglie Du and Hao Wu and Congbao Ran",

year = "2017",

month = aug,

day = "1",

language = "英语",

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TY - JOUR

T1 - Avionics Remain Life Prediction Using Multiple Kernel LS-SVR

AU - Li, Fei

AU - Chen, Ying

AU - Guo, Yangming

AU - Du, Chenglie

AU - Wu, Hao

AU - Ran, Congbao

PY - 2017/8/1

Y1 - 2017/8/1

N2 - Avionics equipments are important parts of the aircraft system, and their failure probability is higher and higher, which will affect the performance of the whole system. A prediction model based on MKLS-SVR is proposed in this paper and used for remain life prediction with an avionic device. The simulation results show that the MKLS-SVR has a higher accuracy, comparing with the traditional model LS-SVR, and it is a practical and effective electronic equipment life prediction method.

AB - Avionics equipments are important parts of the aircraft system, and their failure probability is higher and higher, which will affect the performance of the whole system. A prediction model based on MKLS-SVR is proposed in this paper and used for remain life prediction with an avionic device. The simulation results show that the MKLS-SVR has a higher accuracy, comparing with the traditional model LS-SVR, and it is a practical and effective electronic equipment life prediction method.

KW - Matlab

KW - Root mean square error(RMSE)

KW - Support vector machines(SVM)

KW - Time series

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M3 - 文章

AN - SCOPUS:85028338969

SN - 1000-2758

VL - 35

SP - 724

EP - 728

JO - Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University

JF - Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University

IS - 4

ER -

Avionics Remain Life Prediction Using Multiple Kernel LS-SVR

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Keywords

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