基于信息熵的城轨车辆应变传感器优化布置

针对传统的传感器优化方法在配置数量较多的情况下存在测点聚集的现象，基于信息熵理论分析了优化结果和预测误差间的关系，并提出了一种考虑预测误差的改进优化方法，将归一化模态应变能之和的倒数作为预测误差协方差矩阵的主对角元，非对角元由结合了测点距离和响应水平的指数相关方程表示，以最大化Fisher信息阵行列式值为目标，采用逐步累积法得到最优测点位置.城轨车辆构架有限元计算结果表明，改进的优化方法在三种评价准则的表现上均优于经典方法，并且显著降低了测点聚集效应，证实了改进方法用于转向架构架应变传感器配置的有效性.

Optimal Placement of Strain Sensors for Urban Rail Vehicles Based on Information Entropy

In view of the concentration of measurement points in the traditional sensor optimization method with a large number of configurations， the relationship between optimization results and prediction errors was analyzed based on the information entropy theory， and an improved optimization method considering prediction errors was proposed. The reciprocal of the sum of normalized modal strain energy was used as the principal diagonal element of the prediction error covariance matrix， and the non-diagonal element was expressed by an exponential correlation equation combining the distance of measurement points and the response level. Aiming at maximizing the determinant of Fisher information matrix， the forward sequential sensor placement method was used to obtain the optimal location of measurement points. The finite element analysis results of the urban rail vehicle frames showed that the improved sensor optimization method is superior to the classical method in the performance of three evaluation criteria， and significantly reduces the concentrating effect of measurement points， which proves the effectiveness of the improved method in the configuration of bogie frame strain sensors.