基于频响函数的动车组构架传感器优化布置

彭珍瑞; 张楠; 殷红; 董康立

doi:10.3969/j.issn.0258-2724.20170780

基于频响函数的动车组构架传感器优化布置

doi: 10.3969/j.issn.0258-2724.20170780

基金项目: 国家自然科学基金资助项目（61463028）

详细信息

通讯作者:
彭珍瑞（1972—），男，教授，博士生导师，研究方向为传感器优化布置、模型修正，E-mail：pengzr@mail.lzjtu.cn

中图分类号: O327；U270.14
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- 文章访问数: 403
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- PDF下载量: 2
- 被引次数: 0
出版历程
- 收稿日期: 2017-11-06
- 修回日期: 2018-04-10
- 网络出版日期: 2018-05-31
- 刊出日期: 2019-04-01

Optimal Sensor Placement of EMU Frame Based on Frequency Response Function

摘要

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.
- 传感器优化布置 /
- 构架 /
- 频率响应函数 /
- 独立分量分析 /
- K-均值聚类法 /
- 信息熵
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.
- optimal sensor placement /
- frame /
- frequency response function /
- independent component analysis /
- K-means clustering method /
- information entropy

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图 1 传感器位置布置流程

Figure 1. Flow chart of sensor placement

下载: 全尺寸图片幻灯片

图 2 悬臂梁前3阶模态振幅

Figure 2. First three mode amplitudes of cantilever beam

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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

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图 4 悬臂梁布置效果

Figure 4. Result of sensor placement of cantilever beam

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图 5 PCA与ICA布置结果信息熵对比

Figure 5. Comparison of entropy between PCA and ICA

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图 6 构架Fisher信息矩阵信息熵值

Figure 6. Entropy of Fisher information matrix of frame

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图 7 转向架构架传感器布置效果

Figure 7. Result of sensor placement of frame

下载: 全尺寸图片幻灯片

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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表 3 S355J2G参数

Table 3. Parameters of S355J2G

密度/（g•cm^{– 3}）弹性模量/Pa 泊松比

7.85 2.06 × 10¹¹ 0.28

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