Optimal Sensor Placement of EMU Frame Based on Frequency Response Function
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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.Abstract: 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.
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图 3 悬臂梁Fisher信息矩阵信息熵值
Figure 3. Entropy of Fisher information matrix of cantilever beam
表 1 PCA与ICA特性简要比较
Table 1. Brief comparison of the characteristics between PCA and ICA
项目 PCA ICA 处理信号类型 高斯信号 非高斯信号 统计特性 二阶 高阶 处理效果 得到非相关分量 得到独立分量 
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表 2 悬臂梁布置结果
Table 2. Results of sensor placement of cantilever beam
传感器数目 布置位置 3 1、19、25 4 1、17、19、24 5 1、11、14、17、30 6 1、5、11、14、17、25
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