一种用于起落架减震系统的深度学习机载健康监测方法。

A Deep Learning On-Board Health Monitoring Method for Landing Gear Shock-Absorbing Systems.

作者信息

Li Chunsheng, Chen Wang, Qin Wenfeng

机构信息

Department of Aircraft Engineering, Civil Aviation Flight University of China, Deyang 618307, China.

出版信息

Sensors (Basel). 2025 Apr 27;25(9):2767. doi: 10.3390/s25092767.

DOI:10.3390/s25092767

PMID:40363205

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12074512/

Abstract

This paper proposed a deep learning on-board health monitoring method for landing gear shock-absorbing systems based on dynamic responses during landing. A deep learning model is developed to conduct health monitoring for faults in shock absorbers. A certain general aviation aircraft is focused on in this paper, and a multi-body dynamic model of the nose landing gear is developed to simulate dynamic responses during landing under various health states and various landing conditions for developing a database for the proposed LDGNet. The simulated database is used to conduct model training and to test the performance of the proposed method. The feasibility and effectiveness of the proposed method are verified.

摘要

本文提出了一种基于着陆过程动态响应的起落架减震系统深度学习机载健康监测方法。开发了一种深度学习模型来对减震器故障进行健康监测。本文重点研究了某型通用航空飞机，建立了前起落架的多体动力学模型，以模拟在各种健康状态和各种着陆条件下着陆过程中的动态响应，从而为所提出的LDGNet建立数据库。利用模拟数据库进行模型训练，并测试所提方法的性能。验证了所提方法的可行性和有效性。