基于信息熵的城轨车辆应变传感器优化布置

doi:10.12068/j.issn.1005-3026.2020.03.012

东北大学学报:自然科学版 ›› 2020, Vol. 41 ›› Issue (3): 367-374.DOI: 10.12068/j.issn.1005-3026.2020.03.012

基于信息熵的城轨车辆应变传感器优化布置

张子璠¹，李强¹，刘汉文²，丁然¹

(1.北京交通大学机械与电子控制工程学院，北京100044； 2.东风汽车公司技术中心，武汉430058)

收稿日期:2019-04-30 修回日期:2019-04-30 出版日期:2020-03-15 发布日期:2020-04-10
通讯作者: 张子璠
作者简介:张子璠(1992-)，男，北京人，北京交通大学博士研究生; 李强(1963-)，男，山西太原人，北京交通大学教授，博士生导师.
基金资助:
国家重点基础研究发展计划项目(2016YFB1200403); 国家自然科学基金资助项目(11790281).

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

ZHANG Zi-fan¹， LI Qiang¹， LIU Han-wen²， DING Ran¹

1. School of Mechanical， Electronic and Control Engineering， Beijing Jiaotong University， Beijing 100044， China; 2. Dongfeng Motor Corporation Technology Center， Wuhan 430058， China.

Received:2019-04-30 Revised:2019-04-30 Online:2020-03-15 Published:2020-04-10
Contact: DING Ran
About author:-
Supported by:
-

摘要/Abstract

摘要： 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.

关键词: 城轨车辆, 转向架构架, 传感器优化配置, 应变模态, 信息熵

Abstract: 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.

Key words: urban rail vehicle, bogie frame, optimal sensor configuration, strain mode, information entropy

中图分类号:

U270

张子璠，李强，刘汉文，丁然. 基于信息熵的城轨车辆应变传感器优化布置[J]. 东北大学学报:自然科学版, 2020, 41(3): 367-374.

ZHANG Zi-fan， LI Qiang， LIU Han-wen， DING Ran. Optimal Placement of Strain Sensors for Urban Rail Vehicles Based on Information Entropy[J]. Journal of Northeastern University Natural Science, 2020, 41(3): 367-374.

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基于信息熵的城轨车辆应变传感器优化布置

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

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