• 文献检索
  • 文档翻译
  • 深度研究
  • 学术资讯
  • Suppr Zotero 插件Zotero 插件
  • 邀请有礼
  • 套餐&价格
  • 历史记录
应用&插件
Suppr Zotero 插件Zotero 插件浏览器插件Mac 客户端Windows 客户端微信小程序
定价
高级版会员购买积分包购买API积分包
服务
文献检索文档翻译深度研究API 文档MCP 服务
关于我们
关于 Suppr公司介绍联系我们用户协议隐私条款
关注我们

Suppr 超能文献

核心技术专利:CN118964589B侵权必究
粤ICP备2023148730 号-1Suppr @ 2026

文献检索

告别复杂PubMed语法,用中文像聊天一样搜索,搜遍4000万医学文献。AI智能推荐,让科研检索更轻松。

立即免费搜索

文件翻译

保留排版,准确专业,支持PDF/Word/PPT等文件格式,支持 12+语言互译。

免费翻译文档

深度研究

AI帮你快速写综述,25分钟生成高质量综述,智能提取关键信息,辅助科研写作。

立即免费体验

利用纵向加速度和连续小波变换检测飞机着陆。

Detection of Aircraft Touchdown Using Longitudinal Acceleration and Continuous Wavelet Transformation.

机构信息

Frenchay Campus, The University of the West of England, Coldharbour Lane, Bristol BS16 1QY, UK.

Department of Avionics and Control Systems, Faculty of Mechanical Engineering and Aeronautics, Rzeszów University of Technology, al. Powstańców Warszawy 12, 35-959 Rzeszów, Poland.

出版信息

Sensors (Basel). 2020 Dec 17;20(24):7231. doi: 10.3390/s20247231.

DOI:10.3390/s20247231
PMID:33348653
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7766698/
Abstract

The paper presents a methodology enabling the detection of aircraft touchdowns based on data obtained from accelerometers attached to the structural parts of the airframe in the cockpit or passenger compartment. Precise determination of the moment and place of touchdown of the main landing gear is challenging when analysing parameters such as height, flight speed and rate of descent. During the tests of the I-31T aircraft, it turned out that vibrations of the aircraft structure caused by the contact of the front and main landing gear with the ground have a repetitive character. In particular, this applies to longitudinal acceleration. The use of continuous wavelet analysis (CWT) allowed finding unique periodic features of the landing phenomenon that distinguish it from other forms of vibration occurring in individual flight phases. Ground and flight observations of experimental aeroplane MP-02 Czajka verified the proposed method of virtual touchdown detection. The results presented in this paper justify that this method may find broader application, especially for the light aircraft class.

摘要

本文提出了一种基于安装在驾驶舱或客舱机体结构部件上的加速度计获取的数据来检测飞机触地的方法。在分析高度、飞行速度和下降率等参数时,精确确定主起落架的触地点和触地时刻具有挑战性。在 I-31T 飞机的测试中,事实证明,由于前起落架和主起落架与地面接触而引起的飞机结构振动具有重复性特征。特别是,这适用于纵向加速度。使用连续小波分析 (CWT) 可以找到着陆现象的独特周期特征,将其与在各个飞行阶段发生的其他振动形式区分开来。对实验飞机 MP-02 Czajka 的地面和飞行观测验证了所提出的虚拟触地检测方法。本文提出的方法可以找到更广泛的应用,特别是对于轻型飞机类别。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/c046fd0975d5/sensors-20-07231-g023.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/182d1e56d2f7/sensors-20-07231-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/4715b38ec64a/sensors-20-07231-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/6974476595ec/sensors-20-07231-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/ee74d913e73c/sensors-20-07231-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/43d782ba7c97/sensors-20-07231-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/d3296f3c9b2e/sensors-20-07231-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/e99b318bda25/sensors-20-07231-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/38a269f82b99/sensors-20-07231-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/2c5317fd872a/sensors-20-07231-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/916cf5af457c/sensors-20-07231-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/fdd1cde567cc/sensors-20-07231-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/e775ac6511ed/sensors-20-07231-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/41bcce02dbcc/sensors-20-07231-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/271d79142a41/sensors-20-07231-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/108c6fc42e69/sensors-20-07231-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/3ffb695172cc/sensors-20-07231-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/3e894717717b/sensors-20-07231-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/dfd107ae1ab3/sensors-20-07231-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/349d035c6d4c/sensors-20-07231-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/70d88ead4960/sensors-20-07231-g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/dd83a2323b22/sensors-20-07231-g021.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/ffc1e5e9fa50/sensors-20-07231-g022.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/c046fd0975d5/sensors-20-07231-g023.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/182d1e56d2f7/sensors-20-07231-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/4715b38ec64a/sensors-20-07231-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/6974476595ec/sensors-20-07231-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/ee74d913e73c/sensors-20-07231-g004.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/43d782ba7c97/sensors-20-07231-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/d3296f3c9b2e/sensors-20-07231-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/e99b318bda25/sensors-20-07231-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/38a269f82b99/sensors-20-07231-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/2c5317fd872a/sensors-20-07231-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/916cf5af457c/sensors-20-07231-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/fdd1cde567cc/sensors-20-07231-g011.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/e775ac6511ed/sensors-20-07231-g012.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/41bcce02dbcc/sensors-20-07231-g013.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/271d79142a41/sensors-20-07231-g014.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/108c6fc42e69/sensors-20-07231-g015.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/3ffb695172cc/sensors-20-07231-g016.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/3e894717717b/sensors-20-07231-g017.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/dfd107ae1ab3/sensors-20-07231-g018.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/349d035c6d4c/sensors-20-07231-g019.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/70d88ead4960/sensors-20-07231-g020.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/dd83a2323b22/sensors-20-07231-g021.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/ffc1e5e9fa50/sensors-20-07231-g022.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ee49/7766698/c046fd0975d5/sensors-20-07231-g023.jpg

相似文献

1
Detection of Aircraft Touchdown Using Longitudinal Acceleration and Continuous Wavelet Transformation.利用纵向加速度和连续小波变换检测飞机着陆。
Sensors (Basel). 2020 Dec 17;20(24):7231. doi: 10.3390/s20247231.
2
Application of Operational Load Monitoring System for Fatigue Estimation of Main Landing Gear Attachment Frame of an Aircraft.用于飞机主起落架连接框架疲劳估计的运行载荷监测系统的应用
Materials (Basel). 2021 Nov 1;14(21):6564. doi: 10.3390/ma14216564.
3
IMUMETER-A Convolution Neural Network-Based Sensor for Measurement of Aircraft Ground Performance.IMUMETER—一种基于卷积神经网络的飞机地面性能测量传感器。
Sensors (Basel). 2021 Jul 10;21(14):4726. doi: 10.3390/s21144726.
4
A Multi-Step CNN-Based Estimation of Aircraft Landing Gear Angles.基于多步卷积神经网络的飞机起落架角度估计
Sensors (Basel). 2021 Dec 17;21(24):8440. doi: 10.3390/s21248440.
5
An experimental study of pilots' control characteristics for flight of an STOL aircraft in backside of drag curve at approach and landing.关于短距起降(STOL)飞机在进近和着陆时阻力曲线后侧飞行的飞行员控制特性的实验研究。
Ergonomics. 1992 May-Jun;35(5-6):541-50. doi: 10.1080/00140139208967835.
6
A Self-Attention Integrated Learning Model for Landing Gear Performance Prediction.基于自注意力的起落架性能预测集成学习模型
Sensors (Basel). 2023 Jul 7;23(13):6219. doi: 10.3390/s23136219.
7
A Novel Framework for Qualification of a Composite-Based Main Landing Gear Strut of a Lightweight Aircraft.一种用于轻型飞机复合材料主起落架支柱鉴定的新型框架。
Polymers (Basel). 2023 Mar 11;15(6):1402. doi: 10.3390/polym15061402.
8
Determining Wheel Forces and Moments on Aircraft Landing Gear with a Dynamometer Sensor.使用测力计传感器确定飞机起落架的轮力和轮矩。
Sensors (Basel). 2019 Dec 31;20(1):227. doi: 10.3390/s20010227.
9
Toward Noise Certification during Design: Airframe Noise Simulations for Full-Scale, Complete Aircraft.面向设计阶段的噪声认证:全尺寸完整飞机的机身噪声模拟
CEAS Aeronaut J. 2019 Mar 13;10(1):31-67. doi: 10.1007/s13272-019-00378-1. Epub 2019 Mar 16.
10
Aircraft Landing Gear Retraction/Extension System Fault Diagnosis with 1-D Dilated Convolutional Neural Network.基于一维扩张卷积神经网络的飞机起落架收放系统故障诊断。
Sensors (Basel). 2022 Feb 10;22(4):1367. doi: 10.3390/s22041367.

引用本文的文献

1
Wavelet-Based Identification for Spinning Projectile with Gasodynamic Control Aerodynamic Coefficients Determination.基于小波的旋转弹气动控制气动系数确定的辨识。
Sensors (Basel). 2022 May 27;22(11):4090. doi: 10.3390/s22114090.
2
IMUMETER-A Convolution Neural Network-Based Sensor for Measurement of Aircraft Ground Performance.IMUMETER—一种基于卷积神经网络的飞机地面性能测量传感器。
Sensors (Basel). 2021 Jul 10;21(14):4726. doi: 10.3390/s21144726.

本文引用的文献

1
Determining Wheel Forces and Moments on Aircraft Landing Gear with a Dynamometer Sensor.使用测力计传感器确定飞机起落架的轮力和轮矩。
Sensors (Basel). 2019 Dec 31;20(1):227. doi: 10.3390/s20010227.
2
Application of GNSS/INS and an Optical Sensor for Determining Airplane Takeoff and Landing Performance on a Grassy Airfield.GNSS/INS 和光学传感器在草地机场确定飞机起飞和着陆性能中的应用。
Sensors (Basel). 2019 Dec 12;19(24):5492. doi: 10.3390/s19245492.
3
Wavelet-Variance-Based Estimation for Composite Stochastic Processes.基于小波方差的复合随机过程估计
J Am Stat Assoc. 2013 Sep;108(503):1021-1030. doi: 10.1080/01621459.2013.799920. Epub 2013 Sep 27.