为得到大功率拖拉机传动轴在田间作业工况下的载荷谱,该文针对传统传动系载荷谱编制过程中雨流计数及雨流域外推方法的局限性,提出基于POT(peak over threshold)模型的大功率拖拉机传动轴载荷时域外推方法。首先搭建了拖拉机传动轴扭矩...为得到大功率拖拉机传动轴在田间作业工况下的载荷谱,该文针对传统传动系载荷谱编制过程中雨流计数及雨流域外推方法的局限性,提出基于POT(peak over threshold)模型的大功率拖拉机传动轴载荷时域外推方法。首先搭建了拖拉机传动轴扭矩测试系统,利用无线扭矩传感器采集大功率拖拉机传动轴在田间犁耕作业工况下的载荷数据;基于极值理论建立POT模型,利用灰色关联度分析方法选取最优阈值,确定时域载荷数据中上限、下限阈值分别为497和333 N·m。对超越阈值的极值载荷进行提取并利用广义帕累托分布(generalized pareto distribution,GPD)对极值载荷的分布进行拟合,拟合结果与极值载荷样本之间的相关系数均大于0.99,将生成服从GPD的新极值点取代原样本中的极值点从而实现时域载荷数据的外推。结果表明,GPD能够准确描述大功率拖拉机传动轴载荷超越阈值的分布情况,与雨流域外推方法相比,基于POT模型的载荷时域外推方法不仅可以获得任意里程的载荷时域序列,还能够极大程度保留实测载荷循环的次序,为今后大功率拖拉机传动系的室内载荷谱加载试验提供更加真实可靠的数据支持。展开更多
为研究不同轨道结构形式对地铁车内噪声的影响,测试了列车通过普通整体道床、减振扣件道床、梯形轨枕道床、中档钢弹簧浮置板道床、高档钢弹簧浮置板道床等5种轨道结构形式时的车内噪声。采用A计权声压级对车内噪声时域与频域特性进行分...为研究不同轨道结构形式对地铁车内噪声的影响,测试了列车通过普通整体道床、减振扣件道床、梯形轨枕道床、中档钢弹簧浮置板道床、高档钢弹簧浮置板道床等5种轨道结构形式时的车内噪声。采用A计权声压级对车内噪声时域与频域特性进行分析,探究列车通过5种不同轨道结构时车内噪声分布规律。结果表明:普通整体道床车内噪声瞬时A计权声压级均值为76. 6 d B,减振扣件为82. 3 d B,梯形轨枕道床为77. 2 d B,中档钢弹簧浮置板道床为76. 8 d B,高档钢弹簧浮置板道床为81. 6 d B; 5种轨道结构形式车内噪声A计权声压级频谱差异明显;车内噪声总A计权声压级在空间分布上,同一水平车厢两侧近门窗处比车厢中部约高1. 5 d B,在垂向上声压级随高度的增加逐渐减小,坐高处比站高处噪声总A计权声压级高0. 5 d B。展开更多
Because there is insufficient measurement data when implementing state estimation in distribution networks,this paper proposes an attention-enhanced recurrent neural network(A-RNN)-based pseudo-measurement modeling me...Because there is insufficient measurement data when implementing state estimation in distribution networks,this paper proposes an attention-enhanced recurrent neural network(A-RNN)-based pseudo-measurement modeling metho.First,based on analyzing the power series at the source and load end in the time and frequency domains,a period-dependent extrapolation model is established to characterize the power series in those domains.The complex mapping functions in the model are automatically represented by A-RNNs to obtain an A-RNNs-based period-dependent pseudo-measurement generation model.The distributed dynamic state estimation model of the distribution network is established,and the pseudo-measurement data generated by the model in real time is used as the input of the state estimation model together with the measurement data.The experimental results show that the method proposed can explore in depth the complex sequence characteristics of the measurement data such that the accuracy of the pseudo-measurement data is further improved.The results also show that the state estimation accuracy of a distribution network is very poor when there is a lack of measurement data,but is greatly improved by adding the pseudo-measurement data generated by the model proposed.展开更多
文摘为研究不同轨道结构形式对地铁车内噪声的影响,测试了列车通过普通整体道床、减振扣件道床、梯形轨枕道床、中档钢弹簧浮置板道床、高档钢弹簧浮置板道床等5种轨道结构形式时的车内噪声。采用A计权声压级对车内噪声时域与频域特性进行分析,探究列车通过5种不同轨道结构时车内噪声分布规律。结果表明:普通整体道床车内噪声瞬时A计权声压级均值为76. 6 d B,减振扣件为82. 3 d B,梯形轨枕道床为77. 2 d B,中档钢弹簧浮置板道床为76. 8 d B,高档钢弹簧浮置板道床为81. 6 d B; 5种轨道结构形式车内噪声A计权声压级频谱差异明显;车内噪声总A计权声压级在空间分布上,同一水平车厢两侧近门窗处比车厢中部约高1. 5 d B,在垂向上声压级随高度的增加逐渐减小,坐高处比站高处噪声总A计权声压级高0. 5 d B。
基金supported in part by the National Key Research Program of China(2016YFB0900100)Key Project of Shanghai Science and Technology Committee(18DZ1100303).
文摘Because there is insufficient measurement data when implementing state estimation in distribution networks,this paper proposes an attention-enhanced recurrent neural network(A-RNN)-based pseudo-measurement modeling metho.First,based on analyzing the power series at the source and load end in the time and frequency domains,a period-dependent extrapolation model is established to characterize the power series in those domains.The complex mapping functions in the model are automatically represented by A-RNNs to obtain an A-RNNs-based period-dependent pseudo-measurement generation model.The distributed dynamic state estimation model of the distribution network is established,and the pseudo-measurement data generated by the model in real time is used as the input of the state estimation model together with the measurement data.The experimental results show that the method proposed can explore in depth the complex sequence characteristics of the measurement data such that the accuracy of the pseudo-measurement data is further improved.The results also show that the state estimation accuracy of a distribution network is very poor when there is a lack of measurement data,but is greatly improved by adding the pseudo-measurement data generated by the model proposed.