基于核极限学习机的飞行器故障诊断方法

Jia Song; Ruoling Shi; Xiaohong Guo; Yang Liu

doi:10.16511/j.cnki.qhdxxb.2020.26.005

基于核极限学习机的飞行器故障诊断方法

Translated title of the contribution: KELM based diagnostics for air vehicle faults

Jia Song
, Ruoling Shi
, Xiaohong Guo
, Yang Liu

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

9 Scopus citations

Abstract

A fault diagnosis method based on a kernel extreme learning machine (KELM) was developed to analyze thruster failures in hypersonic aircraft reaction control systems (RCS). The parameters and kernel function were optimized for faults involving aircraft actuator failures. Results using this fast, accurate diagnostic method show that the method is not dependent on the aircraft model and provides fast and accurate diagnoses of aircraft actuator faults using a data-driven process.

Translated title of the contribution	KELM based diagnostics for air vehicle faults
Original language	Chinese (Traditional)
Pages (from-to)	795-803
Number of pages	9
Journal	Qinghua Daxue Xuebao/Journal of Tsinghua University
Volume	60
Issue number	10
DOIs	https://doi.org/10.16511/j.cnki.qhdxxb.2020.26.005
State	Published - 1 Oct 2020

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abstract = "A fault diagnosis method based on a kernel extreme learning machine (KELM) was developed to analyze thruster failures in hypersonic aircraft reaction control systems (RCS). The parameters and kernel function were optimized for faults involving aircraft actuator failures. Results using this fast, accurate diagnostic method show that the method is not dependent on the aircraft model and provides fast and accurate diagnoses of aircraft actuator faults using a data-driven process.",

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AU - Song, Jia

AU - Shi, Ruoling

AU - Guo, Xiaohong

AU - Liu, Yang

PY - 2020/10/1

Y1 - 2020/10/1

N2 - A fault diagnosis method based on a kernel extreme learning machine (KELM) was developed to analyze thruster failures in hypersonic aircraft reaction control systems (RCS). The parameters and kernel function were optimized for faults involving aircraft actuator failures. Results using this fast, accurate diagnostic method show that the method is not dependent on the aircraft model and provides fast and accurate diagnoses of aircraft actuator faults using a data-driven process.

AB - A fault diagnosis method based on a kernel extreme learning machine (KELM) was developed to analyze thruster failures in hypersonic aircraft reaction control systems (RCS). The parameters and kernel function were optimized for faults involving aircraft actuator failures. Results using this fast, accurate diagnostic method show that the method is not dependent on the aircraft model and provides fast and accurate diagnoses of aircraft actuator faults using a data-driven process.

KW - Fault diagnosis

KW - Hypersonic vehicle

KW - Kernel extreme learning machine

KW - Reaction control system

UR - https://www.scopus.com/pages/publications/85089418558

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DO - 10.16511/j.cnki.qhdxxb.2020.26.005

M3 - 文章

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JO - Qinghua Daxue Xuebao/Journal of Tsinghua University

JF - Qinghua Daxue Xuebao/Journal of Tsinghua University

IS - 10

ER -

基于核极限学习机的飞行器故障诊断方法

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