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机构地区:[1]School of Mechanical Engineering, Shanghai University of Engineering Science, Shanghai 201620, P. R. China [2]Department of Instrument Engineering, Shanghai Jiaotong University, Shanghai 200240, P. R. China
出 处:《Journal of Shanghai University(English Edition)》2008年第1期76-79,共4页上海大学学报(英文版)
基 金:Project supported by the Science Foundation of Shanghai Municipal Commission of Science and Technology (Grant No.035115003).Acknowledgment The authors would like to thank Shanghai Yamato Scale Co., Ltd. for providing the experiment site and truck.
摘 要:Dynanfic forces are the main factor that influences the axle weight measurement accuracy of moving vehicle. Empirical mode decomposition (EMD) is presented to separate the dynamic forces contained in the axle weight signal. The concept and algorithm of EMD are introduced. The characteristic of the axle weight signal is analyzed. The method of judging pseudo intrinsic mode function (pseudo-IMF) is presented to improve the weighing accuracy. Numerical simulation and field experiments are conducted to evaluate the performance of EMD. The result shows effectiveness of the proposed method. Maximum weighing errors of the front axle, the rear axle and the gross weight at the speed of 15 km/h or lower are 2.22%, 6.26% and 4.11% respectively.Dynanfic forces are the main factor that influences the axle weight measurement accuracy of moving vehicle. Empirical mode decomposition (EMD) is presented to separate the dynamic forces contained in the axle weight signal. The concept and algorithm of EMD are introduced. The characteristic of the axle weight signal is analyzed. The method of judging pseudo intrinsic mode function (pseudo-IMF) is presented to improve the weighing accuracy. Numerical simulation and field experiments are conducted to evaluate the performance of EMD. The result shows effectiveness of the proposed method. Maximum weighing errors of the front axle, the rear axle and the gross weight at the speed of 15 km/h or lower are 2.22%, 6.26% and 4.11% respectively.
关 键 词:WEIGH-IN-MOTION empirical mode decomposition (EMD) pseudo intrinsic mode function (pseudo-IMF)
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