Fault Diagnosis of Landing Gear Expert System Based on Neural Network

Zeyang Xi, Fangyi Wan, Zhenyu Liang, Xuhui Cui

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The correct diagnosis of the fault degree of aircraft landing gear hydraulic retraction system can help pilots take timely actions to deal with different degrees of failure and avoid personnel and property losses. The purpose of this paper is to propose a kind of growing expert system based on past data by combining with neural network. This paper establishes the simulation model of a certain type of aircraft landing gear hydraulic retraction system and implants different degrees of fault, extracts fault data, normalizes the fault data and trains the corresponding neural network model. The expert system extracts the number of abnormal sensor in the fault data through the neural network, and compares it with the database in the expert system, so that the fault type of the aircraft landing gear hydraulic retraction system can be effectively diagnosed.

Original languageEnglish
Title of host publication2023 Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023
EditorsWei Guo, Steven Li
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798350301359
DOIs
StatePublished - 2023
Event14th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023 - Hangzhou, China
Duration: 12 Oct 202315 Oct 2023

Publication series

Name2023 Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023

Conference

Conference14th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023
Country/TerritoryChina
CityHangzhou
Period12/10/2315/10/23

Keywords

  • aircraft landing gear
  • Expert systems
  • Fault diagnosis
  • Hydraulic retraction system
  • Neural network

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