@inproceedings{f4c7c65bd24a4a8b90552d4a6a84f136,
title = "Fault Diagnosis of Landing Gear Expert System Based on Neural Network",
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.",
keywords = "aircraft landing gear, Expert systems, Fault diagnosis, Hydraulic retraction system, Neural network",
author = "Zeyang Xi and Fangyi Wan and Zhenyu Liang and Xuhui Cui",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 14th IEEE Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023 ; Conference date: 12-10-2023 Through 15-10-2023",
year = "2023",
doi = "10.1109/PHM-HANGZHOU58797.2023.10482795",
language = "英语",
series = "2023 Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Wei Guo and Steven Li",
booktitle = "2023 Global Reliability and Prognostics and Health Management Conference, PHM-Hangzhou 2023",
}