Identification Method of Fatigue Load Characteristics for Reusable Launch Vehicle Engine Based on Gaussian Distribution

XU Zhenliang1, DENG Sichao1, YIN Zhiping2, LUO Jie2, WU Shengbao1

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Journal of Deep Space Exploration ›› 2022, Vol. 9 ›› Issue (5) : 506-511. DOI: 10.15982/j.issn.2096-9287.2022.20210144

Identification Method of Fatigue Load Characteristics for Reusable Launch Vehicle Engine Based on Gaussian Distribution

  • XU Zhenliang1, DENG Sichao1, YIN Zhiping2, LUO Jie2, WU Shengbao1
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Abstract

Reusable launch vehicle is important to reduce the cost of launch service. This paper focuses on the modeling difficulty on the original fatigue load data of reusable launch vehicle engine. In this paper, the root mean square value is selected as the division standard for the original fatigue load data of the reusable launch vehicle. Original data are processed by modified short-time Fourier wave filtering, rain flow cycle counting and Gaussian distribution fitting for the identification and regularization of fatigue load data. Fatigue load data of reusable launch vehicle can be described by Gaussian distribution model. The Gaussian distribution parameter of abnormal fatigue load data is more than 3 times of normal fatigue load data. This method can be used to accurately identify the abnormal fatigue load data. Compared with traditional anomaly data identification methods, this method provides a quantitative index of abnormal data, which is a new analysis method for fatigue load design and real-time fault analysis and location of reusable launch vehicle.

Keywords

reusable launch vehicle / fatigue load / rain flow cycle counting / Gaussian distribution

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XU Zhenliang, DENG Sichao, YIN Zhiping, LUO Jie, WU Shengbao. Identification Method of Fatigue Load Characteristics for Reusable Launch Vehicle Engine Based on Gaussian Distribution. Journal of Deep Space Exploration, 2022, 9(5): 506‒511 https://doi.org/10.15982/j.issn.2096-9287.2022.20210144

References

[1] 韩增尧,邹元杰,朱卫红,等. 航天器力学环境分析与试验技术研究进展[J]. 中国科学 (物理学力学天文学),2019,49(2):6-22
HAN Z Y,ZOU Y J,ZHU W H,et al. Evolution of the spacecraft mechanical environment predication & test technology[J]. Scientia Sinica (Physica,Mechanica & Astronomica),2019,49(2):6-22
[2] 胡雷,胡茑庆,秦国军,等. 涡轮泵状态监控及传感器故障识别的新异类检测方法[J]. 国防科技大学学报,2010,32(2):119-123
HU L,HU Y Q,QIN G J,et al. Novelty detection in turbopump condition monitoring and sensor fault recognition[J]. Journal of National University of Defense Technology,2010,32(2):119-123
[3] HU L,HU N,ZHNAG X,et al. Novelty detection methods for online health monitoring and post data analysis of turbopumps[J]. Journal of Mechanical Science and Technology,2013,27(7):1933-1942
[4] AISWARYA N,PRIYADHARSINI S S,MONI K S. An efficient approach for the diagnosis of faults in turbo pump of liquid rocket engine by employing FFT and time-domain features[J]. Australian Journal of Mechanical Engineering,2018,16(3):163-172
[5] 张炜,张玉祥,黄先祥. 基于神经网络的涡轮泵多故障诊断[J]. 推进技术,2003,24(1):17-20,39
ZHAN W,ZHANG Y X,HUANG X X. Multi-fault diagnosis for turbo-pump based on neural network[J]. Journal of Propulsion Technology,2003,24(1):17-20,39
[6] 李雷,谢立,张永杰,等. 数据挖掘在运载火箭智能测试中的应用[J]. 航空学报,2018,39(S1):86-93
LI L,XIE L,ZHNAG Y J,et al. Application of data mining in intelligence test of launch vehicles[J]. Acta Aeronautica et Astronautica Sinica,2018,39(S1):86-93
[7] 李旭娟,缪炳荣,李国芳,等. 基于结构动态响应的载荷识别研究[J]. 机械制造与自动化,2018,47(1):93-96
LI X J,MIAO B R,LI G F,et al. Research on load identification based on structural dynamic response[J]. Machine Building & Automation,2018,47(1):93-96
[8] 杜晓坤,王栋梁,何顺杰,. 运输类飞机实测载荷飞行任务段划分算法[J]. 科学技术与工程,2017,17(30):352-355
DU X K,WANG D L,HE S J,et al. Division algorithm for flight mission segment of transport aircraft measured load[J]. Science Technology and Engineering,2017,17(30):352-355
[9] 陈亮,洪海明,张音旋. 无人作战飞机载荷谱编制方法[J]. 飞机设计,2017,17(30):352-355
CHEN L,HONG H M,ZHANG Y X. Analysis on technology of load spectrum for unmanned combat aerial vehicle[J]. Aircraft Design,2017,17(30):352-355
[10] 胡雷. 涡轮泵试车数据分析及新异类检测技术研究[D]. 长沙:国防科学技术大学,2005.
HU L. Fault analysis of turbopump test data using novelty detection technology[D]. Changsha:National University of Defense Technology,2005.
[11] KILLICK R,FEARNHEAD P,ECKLEY I A. Optimal detection of changepoints with a linear computational cost[J]. Journal of the American Statistical Association,2012,107(500):1590-1598
[12] GRIFFIN D,LIM J. Signal estimation from modified short-time fourier transform[J]. IEEE Transactions on Acoustics,Speech,and Signal Processing,1984,32(2):236-243
[13] 姚卫星. 结构疲劳寿命分析[M]. 北京:科学出版社,2019:69-72.
[14] 金德新. 改进的雨流计数法应用于随机载荷下的寿命预测[J]. 鞍钢技术,2000(5):55-57
JIN D X. Life prediction of improved rain flow counting method applied under random load[J]. Angang Technology,2000(5):55-57
[15] 蒋祖国,田丁栓,周占廷. 飞机结构载荷/环境谱[M]. 北京:电子工业出版社,2012.
[16] 孙百红,田川. 基于特征频段RMS值的发动机故障实时监测方法[J]. 火箭推进,2019,45(4):74-78
SUN B H,TIAN C. The fault real-time monitoring method for engine based on RMS value of characteristic frequency band[J]. Journal of Rocket Propulsion,2019,45(4):74-78
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