|Table of Contents|

Application of fault detection method based on acoustic signal in launch vehicle(PDF)

《火箭推进》[ISSN:1672-9374/CN:CN 61-1436/V]

Issue:
2021年03期
Page:
1-7
Research Field:
专论与综述
Publishing date:

Info

Title:
Application of fault detection method based on acoustic signal in launch vehicle
Author(s):
LIU Yuwei1; ZHANG Hang2; ZHENG Zhenzhen2; YANG Shuming1; CHENG Yuqiang1
1.College of Aerospace Science and Engineering,National University of Defense Technology,Changsha 410008,China; 2.Xi'an Aerospace Propulsion Institute,Xi'an 710100,China
Keywords:
acoustic signal;  signal processing;  fault detection;  launch vehicle;  health monitoring
PACS:
V431
DOI:
-
Abstract:
The fault detection based on acoustic signal can collect the sound signal of working components through the non-contact sensor.Through the signal acquisition and signal processing,the acoustic signal can effectively characterize the working status of components,and then realize the fault detection.With the continuous development of acoustic signals in equipment fault detection and diagnosis,the acoustic signal has begun to be used for rocket fault detection to detect the state of rocket engines and other components,and some research results have been achieved.This paper summarizes the general method of fault detection based on acoustic signal,analyzes the application status of fault detection method based on acoustic signal in the field of launch vehicle health monitoring,reviews the related technologies of fault detection method based on acoustic signal in the field of rocket health monitoring,and finally looks forward to the main development trend.

References:

[1] 孙少军.重型车用发动机振动与噪声控制的理论与应用研究[D].天津: 天津大学,2008.
[2] 武孟,张小辉.铁路客车空调通风机常见振动故障诊断[J].制冷空调与电力机械,2008,29(5): 71-73.
[3] 沈阳阳.基于振动信号分析的风力发电机轴承故障诊断[D].乌鲁木齐: 新疆大学,2015.
[4] 高星星.基于循环平稳的滚动轴承故障诊断方法研究[D].大连: 大连交通大学,2016.
[5] 潘静.行程开关早期故障诊断研究[D].杭州: 中国计量大学,2017.
[7] PARK J,KIM S,CHOI J H,et al.Frequency energy shift method for bearing fault prognosis using microphone sensor[J].Mechanical Systems and Signal Processing,2021,147: 107068.
[8] 于华森,黄民.滚动轴承声信号故障诊断[J].北京信息科技大学学报(自然科学版),2018,33(6): 72-76.
[9] 郝洪涛,倪凡凡,丁文捷.基于声音信号的托辊故障诊断方法[J].噪声与振动控制,2019,39(3): 187-192.
[10] 张郑武,冯志鹏,陈小旺.基于高阶同步压缩变换的行星齿轮箱声音信号共振频带特征提取[J].工程科学学报,2020,42(8): 1048-1054.
[11] BASTEN T G H,BREE H E D,DRUYVESTEYN W F,et al.Multiple incoherent sound source localiza-tion using a single vector sensor[C]//16th International Congress on Sound and Vibration.krakow, Poland:[s.n.],2009.
[12] JING W Q,COMESAA D F,DAVID P C.Sound source localization using a single acoustic vector sensor and multichannel microphone phased arrays[Z].2014.
[13] KOTUS J.Multiple sound sources localization in free field using acoustic vector sensor[J].Multimedia Tools and Applications,2015,74(12): 4235-4251.
[14] 赖少将,李舜酩.基于近场声阵列的旋转机械噪声源识别[J].噪声与振动控制,2016,36(3): 122-126.
[15] 马超,王少红,徐小力.基于EEMD的声阵列滚动轴承故障诊断[J].电子测量与仪器学报,2017,31(9): 1379-1384.
[16] 卢显达.基于SWT与麦克风阵列的滚动轴承声学诊断方法[D].广州: 广州大学,2018.
[17] 王宁.基于麦克风阵列的机电设备故障声源定位系统[D].天津: 河北工业大学,2018.
[18] 宁培培.基于麦克风阵列的机械故障诊断研究[D].成都: 电子科技大学,2019.
[19] 马波,于功也,闫戈.基于麦克风阵列的机车车辆转向架跑合试验的故障监测研究[J].机车电传动,2019(6): 95-99.
[20] 王鑫.基于小波变换的机械轴承磨损故障特征提取方法研究[D].成都: 西南交通大学,2016.
[21] 彭志科,褚福磊.小波变换在故障诊断中的应用综述与展望[C]// 2002年全国振动工程及应用学术会议.北京:中国振动工程学会,2002.
[22] 王婷.EMD算法研究及其在信号去噪中的应用[D].哈尔滨: 哈尔滨工程大学,2010.
[23] LEE D H,AHN J H,KOH B H.Fault detection of bearing systems through EEMD and optimization algorithm[J].Sensors,2017,17(11): 2477.
[24] CHEN L,CHEN G C,ZHU Z Q.Harmonic analysis method based on CEEMD-HT algorithm[J].Electric Power and Engineering,2017,33(1):61-66.
[25] TORRES M E,COLOMINAS M A,SCHLOTTHAUER G,et al.A complete ensemble empirical mode decomposition with adaptive noise[C]//2011 IEEE International Conference on Acoustics,Speech and Signal Processing(ICASSP).Prague,Czech Republic:IEEE,2011.
[26] 李春雷,董志学,王新杰.发电机声音检测与故障诊断研究[J].内蒙古工业大学学报(自然科学版),2015,34(3): 201-208.
[27] 赵殿全,李金龙,谢蓓敏.基于阀厅智能巡检机器人的设备声音故障识别算法[J].电子设计工程,2016,24(21): 63-65.
[28] 朱星明.基于FFT和BP神经网络的变压器振动噪声特征识别[J].科学技术创新,2018(29): 34-36.
[29] 李倩.BP神经网络的舰船辐射噪声识别[J].舰船科学技术,2019,41(24): 22-24.
[30] 余长厅,黎大健,汲胜昌,等.基于振动噪声及BP神经网络的变压器故障诊断方法研究[J].高压电器,2020,56(6): 256-261.
[31] WANG Y S,LIU N N,GUO H,et al.An engine-fault-diagnosis system based on sound intensity analysis and wavelet packet pre-processing neural network[J].Engineering Applications of Artificial Intelligence,2020,94: 103765.
[32] 江毓,郑燕萍,张新,等.基于改进BP神经网络的电机异音诊断[J].重庆理工大学学报(自然科学),2020,34(1): 242-246.
[33] 王万俊.装甲车发动机故障诊断系统的研究[D].武汉: 武汉理工大学,2009.
[34] 高瑞鹏,尚春阳,江航.遗传算法结合小波神经网络的列车车轮扁疤故障检测方法[J].西安交通大学学报,2013,47(9): 88-91.
[35] 刘龙.基于小波神经网络的带式输送机托辊故障检测研究[J].大科技,2019(4):204.
[36] 杭州安脉盛智能技术有限公司.一种基于改进小波包和深度学习的变压器声音异常检测方法:CN201911315352.5[P].2020-06-09.
[37] 杜设亮,傅建中,陈子辰,等.基于BP神经网络的齿轮故障诊断系统研究[J].机电工程,1999,16(5): 81-83.
[38] 马志远,王洪波,孙晴.基于EEMD样本熵与小波神经网络的汽车关门声品质预测[J].噪声与振动控制,2019,39(3): 122-127.
[39] 李宏亮,黄民,高宏,等.基于声信号的滚动轴承故障诊断[J].组合机床与自动化加工技术,2016(7): 86-88.
[40] 申博文,王华庆,唐刚,等.基于MCKD与CEEMDAN的声信号故障特征提取方法[J].复旦学报(自然科学版),2019,58(3): 385-392.
[41] 李静娇.基于声学信号的滚动轴承故障诊断研究及应用[D].石家庄: 石家庄铁道大学,2017.
[42] 高阳.螺杆压缩机噪声控制措施的研究[J].机电信息,2011(6): 3.
[43] FARINHOLT K M,LEO D J.Acoustic modeling and control of conical enclosures[J].Journal of Vibrationand Acoustics,2003,125(1): 2-11.
[44] PARRONDO J,PÉREZ J,BARRIO R,et al.A simple acoustic model to characterize the internal low frequency sound field in centrifugal pumps[J].Applied Acoustics,2011,72(1): 59-64.
[45] PARRONDO-GAYO J L, GONZLEZ-PREZ J, FERN NDEZ-FRANCOS J. The effect of the operating point on the pressure fluctuations at the blade passage frequency in the volute of a centrifugal pump[J]. Journal of Fluids Engineering, 2002, 124(3): 784-790.
[46] PARRONDO J, BARRIO R, GONZALEZ J, et al. Discrete noise sources in a centrifugal pump operating at partial load[C]// 12th International congress on sound and vibration. Lisbon, Portugal:[s.n.], 2005.
[47] 任方,张正平,李海波,等.运载火箭起飞噪声环境缩比模型试验方法[J].宇航学报,2015,36(3): 344-350.
[48] 黄怀德.振动工程[M].北京:中国宇航出版社,1995.
[49] 张正平,任方,冯秉初.飞机噪声技术研究: 工程解决方法[J].航空学报,2008,29(5): 1207-1212.
[50] 北京宇航系统工程研究所.基于声学监测的发动机状态监测与诊断系统及实现方法:CN201910219292.0[P].2019-07-09.
[51] 张少博,王乃世,陈海峰,等.基于声压测量的阀门故障检测方法研究[J].火箭推进,2015,41(4): 100-104.ZHANG S B,WANG N S,CHEN H F,et al.Method of valve fault detection based on sound pressure measurement[J].Journal of Rocket Propulsion,2015,41(4): 100-104.
[52] ZHANG Z L,ZANG S S,GE B,et al.Acoustic diagnostics applications in the study of the oscillation combustion in lean premixed pre-evaporation combustor[C]// ASME 2017 International Mechanical Engineering Congress and Exposition.[S.l.]: ASME,2017.

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