Sensor Fault Diagnosis of Aero Engine Control System Based on Honey Badger Optimizer

Yingxue Chen; Linfeng Gou; Huihui Li; Jiayi Wang

doi:10.1016/j.ifacol.2022.05.040

Sensor Fault Diagnosis of Aero Engine Control System Based on Honey Badger Optimizer

Yingxue Chen
, Linfeng Gou
, Huihui Li
, Jiayi Wang

School of Power and Energy

Northwestern Polytechnical University Xian

Research output: Contribution to journal › Conference article › peer-review

6 Scopus citations

Abstract

Traditional redundancy detection methods are challenging to achieve fault detection due to high modeling complexity. Aiming at this problem, a data-driven engine sensor fault diagnosis method based on data is established. This paper proposes an intelligent fault diagnosis method based on meta-heuristic optimization, which uses honey badger optimization for feature selection to reduce redundancy information and improve classification accuracy in the problem of aero-engine sensor fault diagnosis. The accuracy of the proposed algorithm is significantly better than the traditional method.

Original language	English
Pages (from-to)	228-233
Number of pages	6
Journal	IFAC-PapersOnLine
Volume	55
Issue number	3
DOIs	https://doi.org/10.1016/j.ifacol.2022.05.040
State	Published - 2022
Event	16th IFAC Symposium on Large Scale Complex Systems: Theory and Applications LSS 2022 - Xi'an, China Duration: 22 Apr 2022 → 24 Apr 2022

Keywords

aero-engine sensor
control system
fault diagnosis
honey badger optimization
meta-heuristic method

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10.1016/j.ifacol.2022.05.040

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title = "Sensor Fault Diagnosis of Aero Engine Control System Based on Honey Badger Optimizer",

abstract = "Traditional redundancy detection methods are challenging to achieve fault detection due to high modeling complexity. Aiming at this problem, a data-driven engine sensor fault diagnosis method based on data is established. This paper proposes an intelligent fault diagnosis method based on meta-heuristic optimization, which uses honey badger optimization for feature selection to reduce redundancy information and improve classification accuracy in the problem of aero-engine sensor fault diagnosis. The accuracy of the proposed algorithm is significantly better than the traditional method.",

keywords = "aero-engine sensor, control system, fault diagnosis, honey badger optimization, meta-heuristic method",

author = "Yingxue Chen and Linfeng Gou and Huihui Li and Jiayi Wang",

note = "Publisher Copyright: {\textcopyright} 2022 Elsevier B.V.. All rights reserved.; 16th IFAC Symposium on Large Scale Complex Systems: Theory and Applications LSS 2022 ; Conference date: 22-04-2022 Through 24-04-2022",

year = "2022",

doi = "10.1016/j.ifacol.2022.05.040",

language = "英语",

volume = "55",

pages = "228--233",

journal = "IFAC-PapersOnLine",

issn = "2405-8963",

publisher = "Elsevier B.V.",

number = "3",

}

TY - JOUR

T1 - Sensor Fault Diagnosis of Aero Engine Control System Based on Honey Badger Optimizer

AU - Chen, Yingxue

AU - Gou, Linfeng

AU - Li, Huihui

AU - Wang, Jiayi

PY - 2022

Y1 - 2022

N2 - Traditional redundancy detection methods are challenging to achieve fault detection due to high modeling complexity. Aiming at this problem, a data-driven engine sensor fault diagnosis method based on data is established. This paper proposes an intelligent fault diagnosis method based on meta-heuristic optimization, which uses honey badger optimization for feature selection to reduce redundancy information and improve classification accuracy in the problem of aero-engine sensor fault diagnosis. The accuracy of the proposed algorithm is significantly better than the traditional method.

AB - Traditional redundancy detection methods are challenging to achieve fault detection due to high modeling complexity. Aiming at this problem, a data-driven engine sensor fault diagnosis method based on data is established. This paper proposes an intelligent fault diagnosis method based on meta-heuristic optimization, which uses honey badger optimization for feature selection to reduce redundancy information and improve classification accuracy in the problem of aero-engine sensor fault diagnosis. The accuracy of the proposed algorithm is significantly better than the traditional method.

KW - aero-engine sensor

KW - control system

KW - fault diagnosis

KW - honey badger optimization

KW - meta-heuristic method

UR - https://www.scopus.com/pages/publications/85134541801

U2 - 10.1016/j.ifacol.2022.05.040

DO - 10.1016/j.ifacol.2022.05.040

M3 - 会议文章

AN - SCOPUS:85134541801

SN - 2405-8963

VL - 55

SP - 228

EP - 233

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

IS - 3

T2 - 16th IFAC Symposium on Large Scale Complex Systems: Theory and Applications LSS 2022

Y2 - 22 April 2022 through 24 April 2022

ER -

Sensor Fault Diagnosis of Aero Engine Control System Based on Honey Badger Optimizer

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Keywords

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