基于独立分量分析的多车响应信号分离
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摘要
针对桥梁动态称重系统(Bridge weigh-in-motion,BWIM)中多车同时过桥情形下的轴重识别问题,提出利用独立分量分析(Independent Component Analysis,ICA)对多车引起的应变信号进行分离的方法。通过ICA分离多车产生的应变响应信号,得到单辆车对应的应变响应信号,即可利用传统BWIM算法进行轴重识别。相较于改进传统BWIM算法处理多车过桥问题的方法 (如影响面法),该方法对桥梁结构信息依赖度低,因此更具有普适性。通过双车混合仿真信号进行实验,论文比较了几种ICA算法的分离效果。结果表明权值调整二阶盲辨识(WASOBI)算法的分离效果最好,为BWIM中的多车轴重识别问题提供了一种高效解决方法。
Aiming at solving the problem that bridge weigh-in-motion( BWIM) system can' t accurately identify the axles weight under multiple presence of passing heavy vehicles, Independent Component Analysis( ICA) is brought up to separate the strain data induced by multiple presence. Once the strain data caused by a single vehicle is identified,traditional BWIM algorithm can be utilized to calculate the axle weights of the identified vehicle. Comparing with other algorithms, like influence surface, embedded in modified BWIM, ICA is less dependent on the bridge information, and consequently more adaptable. Hence,three ICA algorithms have been involved in to separate the mixed simulated strain signals caused by a multiple presence case with two vehilces on the bridge. Comparison of the seperated signals illustrate that weights-adjusted second-order blind identification( WASOBI)algorithm can more effectively sepearte combined signals and provides a promising potential for BWIM to solve the multiple presence problems.
Aiming at solving the problem that bridge weigh-in-motion( BWIM) system can' t accurately identify the axles weight under multiple presence of passing heavy vehicles, Independent Component Analysis( ICA) is brought up to separate the strain data induced by multiple presence. Once the strain data caused by a single vehicle is identified,traditional BWIM algorithm can be utilized to calculate the axle weights of the identified vehicle. Comparing with other algorithms, like influence surface, embedded in modified BWIM, ICA is less dependent on the bridge information, and consequently more adaptable. Hence,three ICA algorithms have been involved in to separate the mixed simulated strain signals caused by a multiple presence case with two vehilces on the bridge. Comparison of the seperated signals illustrate that weights-adjusted second-order blind identification( WASOBI)algorithm can more effectively sepearte combined signals and provides a promising potential for BWIM to solve the multiple presence problems.
引文
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[14] Zhao H,Uddin N,O’Brien E J,et al. Identification of Vehicular Axle Weights with a Bridge Weigh-in-Motion System Considering Transverse Distribution of Wheel Loads[J]. Journal of Bridge Engineering,2014,19(3):04013008.
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[16]赵华,谭承君,张龙威,等.基于小波变换的桥梁动态称重系统车轴高精度识别研究[J].湖南大学学报(自科版),2016,43(7):111-119.
[17] A. Cichocki,S. Amari,K. Siwek,T. Tanaka,Anh Huy Phan et al.,ICALAB Toolboxes[CP/OL]. Laboratory for Advanced Brain Signal Processing,Hirosawa,Wako Saitama,JAPAN,2007.http://www. bsp. brain. riken. jp/ICALAB.
[18]唐炬,彭莉,谢颜斌.一种用于抑制白噪声的分层复阈值算法[J].中国电机工程学报,2007,27(21):25-30.
[2] Ojio T,Yamada K,Shinkai H. BWIM Systems Using Truss Bridges[C]//International Conference on Bridge Management. 2000.
[3] QUILLIGAN,Michael. Bridge Weigh-in-Motion:Development of a 2-D multi-vehicle algorithm[J]. Trita-BKN. Bulletin,2003.
[4] Rowley C W,Obrien E J,Gonzalez A,et al. Experimental Testing of a Moving Force Identification Bridge Weigh-in-Motion Algorithm[J]. Experimental Mechanics,2009,49(5):743-746.
[5] Jacob B,O'Brien E J. European Specification On Weigh-In-Motion Of Road Vehicles(COST323)[C]//European Conference on Weigh-In-Motion of Road Vehicles. 1998.
[6] Quilligan M,Karoumi R,Obrien E J. Development and Testing of a2-Dimensional Multi-Vehicle Bridge-WIM Algorithm[C]//International Conference on Weigh-In-Motion. 2002.
[7] Grabau M A C. Analysis of sampling and multi-vehicle separation for BWIM systems[J]. 2015.
[8] Rinen A,Oja E. A fast fixed-point algorithm for independent component analysis[M]. MIT Press,1997.
[9] Yeredor A. Blind separation of Gaussian sources via second-order statistics with asymptotically optimal weighting[J]. 2000,7(7):197-200.
[10] Tichavsky P,Doron E,Yeredor A,et al. A computationally affordable implementation of an asymptotically optimal BSS algorithm for ar sources[C]//Signal Processing Conference,2006,European. IEEE,2010:1-5.
[11] Koldovsky Z,Tichavsky P,Oja E. Efficient Variant of Algorithm Fast ICA for Independent Component Analysis Attaining the Cramér-Rao Lower Bound[J]. IEEE Trans Neural Netw,2006,17(5):1265-77.
[12] Tichavsky P,Koldovsky Z,Yeredor A,et al. A hybrid technique for blind separation of non-gaussian and time-correlated sources using a multicomponent approach[J]. IEEE Transactions on Neural Networks,2008,19(3):421-430.
[13] Cardoso J. Jacobi angles for simultaneous diagonalization[J]. Siam J. mat. anal. appl,1996,17(1):161-164.
[14] Zhao H,Uddin N,O’Brien E J,et al. Identification of Vehicular Axle Weights with a Bridge Weigh-in-Motion System Considering Transverse Distribution of Wheel Loads[J]. Journal of Bridge Engineering,2014,19(3):04013008.
[15] Znidaric A,Lavric I,Kalin J,et al. SiWIM bridge weigh-inmotion manual[M]. Slovenian:Slovenian National Building and Civil Engineering Institute,2005:2-24.
[16]赵华,谭承君,张龙威,等.基于小波变换的桥梁动态称重系统车轴高精度识别研究[J].湖南大学学报(自科版),2016,43(7):111-119.
[17] A. Cichocki,S. Amari,K. Siwek,T. Tanaka,Anh Huy Phan et al.,ICALAB Toolboxes[CP/OL]. Laboratory for Advanced Brain Signal Processing,Hirosawa,Wako Saitama,JAPAN,2007.http://www. bsp. brain. riken. jp/ICALAB.
[18]唐炬,彭莉,谢颜斌.一种用于抑制白噪声的分层复阈值算法[J].中国电机工程学报,2007,27(21):25-30.