基于频响函数的动车组构架传感器优化布置
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摘要
为了实现传感器布置的位置和数量双重优化,提出了一种基于频率响应函数的传感器优化布置方法.首先,对结构进行模态分析并提取模态振型,计算结构的频率响应函数;然后,基于独立分量分析和K-均值聚类法优化传感器布置位置;最后,计算不同传感器数量对应的布置结果的Fisher信息矩阵及其熵,信息熵的极小值对应的传感器数量即为传感器布置最优的数量. CRH3型动车组构架传感器优化布置结果表明,传感器均布置在符合国际铁路联盟标准UIC615-4规定的所有工况下构架产生最大应力位置附近,并且布置结果可以使数量有限的传感器获得的信息量最大化.
To realize both location and quantity optimization,a method of optimal sensor placement based on a frequency response function is proposed. First,the structure modal analysis was conducted and mode shapes were extracted, and the frequency response function of the structure was calculated. Then, based on independent component analysis and the K-means clustering method, the sensors ' positions were optimized. Finally, the Fisher information matrices and their entropies corresponding to the placement results with different numbers of sensors were calculated. The number of sensors corresponding to minimum information entropy is the optimal number. The simulation results of the CRH3 frame show that all sensors are placed near the maximum stress positions,meeting the UIC615-4 standard,and also show that the result of sensor placement can maximize the information with limited sensors.
To realize both location and quantity optimization,a method of optimal sensor placement based on a frequency response function is proposed. First,the structure modal analysis was conducted and mode shapes were extracted, and the frequency response function of the structure was calculated. Then, based on independent component analysis and the K-means clustering method, the sensors ' positions were optimized. Finally, the Fisher information matrices and their entropies corresponding to the placement results with different numbers of sensors were calculated. The number of sensors corresponding to minimum information entropy is the optimal number. The simulation results of the CRH3 frame show that all sensors are placed near the maximum stress positions,meeting the UIC615-4 standard,and also show that the result of sensor placement can maximize the information with limited sensors.
引文
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[12]International Union of Railways.Motive power unitsbogies and running gear-bogie frame structure strength tests:CodeUIC615-4[S].Paris:International Union of Railways,2003.
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[14]张楠,彭珍瑞,殷红,等.基于熵值法的目标模态最优数目确定新方法[J].铁道科学与工程学报,2018,15(2):483-488.ZHANG Nan,PENG Zhenrui,YIN Hong,et al.Entropy-based approach for determining the optimal number of target modes[J].Journal of Railway Science and Engineering,2018,15(2):483-488.
[2]YI T,LI H,SONG G,et al.Optimal sensor placement for health monitoring of high-rise structure using adaptive monkey algorithm[J].Structural Control&Health Monitoring,2015,22(4):667-681.
[3]詹杰子,余岭.传感器优化布置的有效独立-改进模态应变能方法[J].振动与冲击,2017,36(1):82-87.ZHAN Jiezi,YU Ling.An effective independenceimproved modal strain energy method for optimal sensor placement[J].Journal of Vibration And Shock,2017,36(1):82-87.
[4]陈春强,陈前.电流变夹层板控制系统的传感器优化布置[J].振动与冲击,2017,36(4):132-138.CHEN Chunqiang,CHEN Qian.Optimal sensor placement in the control system of a sandwich plate with electro-rheological fluid[J].Journal of Vibration and Shock,2017,36(4):132-138.
[5]王伟,游鹏辉,钟万里,等.薄壁小直径管道导波探伤的传感器布置方案优化[J].中南大学学报(自然科学版),2016,47(7):2254-2259.WANG Wei,YOU Penghui,ZHONG Wanli,et al.Optimization of guided wave sensors distribution along thin-walled small-diameter pipe[J].Journal of Central South University(Science and Technology),2016,47(7):2254-2259.
[6]LI S,ZHANG H,LIU S,et al.Optimal sensor placement using FRFs-based clustering method[J].Journal of Sound&Vibration,2016,385(12):69-80.
[7]许昕,潘宏侠,潘铭志.独立分量分析在自动机振动信号处理中的应用[J].振动、测试与诊断,2016,36(1):120-125.XU Xin,PAN Hongxia,PAN Mingzhi.Estimation of the unbalance of rotor system based on robust regression analysis[J].Journal of Vibration,Measurement&Diagnosis,2016,36(1):120-125.
[8]曾生根,王小敏,范瑞彬,等.基于独立分量分析的遥感图像分类技术[J].遥感学报,2004,8(2):150-157.ZENG Shenggen,WANG Xiaomin,FAN Ruibin,et al.Remote image classification based on independent component analysis[J].Journal of Remote Sensing,2004,8(2):150-157.
[9]陈法法,杨勇,马婧华,等.信息熵与优化LS-SVM的轴承性能退化模糊粒化预测[J].仪器仪表学报,2016,37(4):779-787.CHEN Fafa,YANG Yong,MA Jinghua,et al.Fuzzy granulation prediction for bearing performance degradation based on information entropy and optimized LS-SVM[J].Chinese Journal of Scientific Instrument,2016,37(4):779-787.
[10]KAMMER D C.Sensor placement for on orbit modal identification of large space structures[J].Journal of Guidance,Control,and Dynamics,1991,14(2):252-259.
[11]何浩祥,闫维明,张爱林.面向结构健康监测的传感器数量及位置优化研究[J].振动与冲击,2008,27(9):131-134.HE Haoxiang,YAN Weiming,ZHANG Ailin.Dynamic characteristic analysis of fluid-structure interaction in steam generator tubes[J].Journal of Vibration and Shock,2008,27(9):131-134.
[12]International Union of Railways.Motive power unitsbogies and running gear-bogie frame structure strength tests:CodeUIC615-4[S].Paris:International Union of Railways,2003.
[13]李德春,何龙军,陈媛媛,等.基于改进粒子群算法的应变传感器优化布置[J].振动、测试与诊断,2014,34(4):610-615.LI Dechun,HE Longjun,CHEN Yuanyuan,et al.Optimal strain sensor placement based on an improved particle swarm optimization algorithm[J].Journal of Vibration,Measurement&Diagnosis,2014,34(4):610-615.
[14]张楠,彭珍瑞,殷红,等.基于熵值法的目标模态最优数目确定新方法[J].铁道科学与工程学报,2018,15(2):483-488.ZHANG Nan,PENG Zhenrui,YIN Hong,et al.Entropy-based approach for determining the optimal number of target modes[J].Journal of Railway Science and Engineering,2018,15(2):483-488.