基于磁弹效应的传感器的温度补偿方法研究

发布时间：2018-05-10 20:17

本文选题：磁弹效应传感器 + BP神经网络　；参考：《安徽理工大学》2017年硕士论文

【摘要】：桥梁的索力健康至关重要,它是大跨度桥梁的受力构件最主要的一部分,对于索力的实时捡测一直是桥梁安全问题的核心之一。基于磁弹效应的索力检测是一种新型等优点。目前,虽然已有基于磁弹效应的索力传感器应用于实际的桥梁索力检测,但是这种索力传感器的研制和索力测量是基于大量实验数据的统计和分析,缺乏理论上对影响钢缆索索力测量的磁化特性、应力变化和温度变化的系统分析。根据铁磁材料的逆磁致伸缩效应,铁磁材料在磁场中会被磁化,如果铁磁材料受到外力(如拉力)的作用,那么材料的磁化参数(磁导率μ)同时将会发生改变,我们把该现象为磁弹效应。磁弹效应传感器的原理是磁导率变化与索力存在对应关系,通过测量磁导率变化就能得出索力。但磁导率可能会随着温度的变化而变化,此时将影响到传感器的测量结果。因此,磁弹效应传感器测量结果受温度的影响,对磁弹效应传感器进行温度补偿是很有必要的。温度影响磁弹索力传感器索力监测这一问题的研究尚不足,对于整个系统的温度影响机理,以及合理有效的温度控制措施,还需进一步系统的深入研究。本文采用的是BP神经网络法和RBF神经网络法降低温度的干扰,还原真实的受力信号。用MATLAB对BP网络法和RBF神经网络法两种温度算法进行仿真,并比较二者的补偿效果。结果表明:提出了 BP神经网络法和RBF神经网络法对磁弹效应传感器进行温度补偿,其中BP神经网络补偿精度最高可达98%,补偿效果较好,提高了磁弹传感器的准确性和稳定性。
[Abstract]:The bridge's cable force health is very important, it is the most important part of the long-span bridge's mechanical components, and the real-time measurement of the cable force is always one of the key problems of bridge safety. The cable force detection based on magnetoelastic effect is a new type of cable force detection. At present, although the cable force sensor based on magnetoelastic effect has been applied to the actual bridge cable force detection, the development of the cable force sensor and the cable force measurement are based on the statistics and analysis of a large number of experimental data. There is no systematic analysis of the magnetization characteristics, stress changes and temperature changes affecting cable force measurement in theory. According to the inverse magnetostriction effect of ferromagnetic material, ferromagnetic material will be magnetized in the magnetic field. If the ferromagnetic material is subjected to external force (such as pull force), the magnetization parameter (permeability 渭) of the material will change at the same time. We refer to this phenomenon as a magnetoelastic effect. The principle of magnetoelastic effect sensor is that there is a corresponding relationship between the change of permeability and the cable force, and the cable force can be obtained by measuring the change of permeability. However, the permeability may vary with the change of temperature, which will affect the measurement results of the sensor. Therefore, the results of magnetoelastic effect sensor are affected by temperature, so it is necessary to compensate the temperature of magnetoelastic effect sensor. The research on the temperature effect of the magnetoelastic cable force sensor is not enough. The mechanism of temperature influence and the reasonable and effective temperature control measures need to be further studied. In this paper, BP neural network and RBF neural network are used to reduce the interference of temperature and to reduce the real force signal. Two temperature algorithms, BP network method and RBF neural network method, are simulated by MATLAB and their compensation effects are compared. The results show that BP neural network method and RBF neural network method are proposed to compensate the temperature of magnetoelastic effect sensor. The compensation accuracy of BP neural network is up to 98, and the compensation effect is good, which improves the accuracy and stability of magnetoelastic sensor.
【学位授予单位】：安徽理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：U446

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