朱中洋,孙光民,吴斌,何存富,刘秀成.基于低分辨率磁滞变化曲线的杆件拉力测量[J].仪器仪表学报,2017,38(10):2555-2563
基于低分辨率磁滞变化曲线的杆件拉力测量
Tension measurement method for rod like structure based on low resolution curve of hysteresis loop change
  
DOI:
中文关键词:  磁弹  磁滞回线  低分辨率磁滞变化曲线  神经网络
英文关键词:magneto elastic  hysteresis loops  low resolution curve of hysteresis loop change (CHLC)  neural network
基金项目:国家自然科学基金(11527801,61305026)项目资助
作者单位
朱中洋 北京工业大学信息学部北京100124 
孙光民 北京工业大学信息学部北京100124 
吴斌 北京工业大学机械工程与应用电子技术学院北京100124 
何存富 北京工业大学机械工程与应用电子技术学院北京100124 
刘秀成 北京工业大学机械工程与应用电子技术学院北京100124 
AuthorInstitution
Zhu Zhongyang Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China 
Sun Guangmin Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China 
Wu Bin College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing 100124, China 
He Cunfu College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing 100124, China 
Liu Xiucheng College of Mechanical Engineering and Applied Electronics Technology, Beijing University of Technology, Beijing 100124, China 
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中文摘要:
      根据磁弹基本原理可知,不同拉力作用下杆件材料的磁滞回线存在差异,据此提出了一种磁弹拉力测量改进方法。该方法先采用双套筒线圈式磁弹传感器采集不同拉力下杆件磁滞回线信号,并利用磁滞变化曲线衡量力对磁滞回线上每一点的影响,应用小波分析对磁滞变化曲线降维得到不同拉力下的低分辨率磁滞变化曲线特征,输入到神经网络进行训练,从而获得低分辨率磁滞变化曲线与拉力的映射关系。通过实验分析表明,磁滞变化曲线可以从本质上直观地反映拉力对磁滞回线上每一个点的影响。低分辨率磁滞变化曲线特征不仅包含着完整的拉力对磁滞回线每一点影响的信息而且特征维数低。应用基于低分辨率磁滞变化曲线和神经网络的拉力测量方法,无需分析灵敏度曲线和拟合确定系数曲线就可确定反映拉力的特征,可以实现多特征对拉力的拟合。接着,比较了误差反向传播神经网络(BPNN)、径向基神经网络(RBFNN)和利用线性插值样本训练的RBF神经网络对拉力的预测性能,发现利用线性插值样本训练后的RBF神经网络的预测效果,优于BP神经网络和没采用线性插值样本训练的RBF神经网络。最后,将基于低分辨率磁滞变化曲线和采用线性插值样本训练的RBF神经网络的拉力测量方法集成于双套筒线圈式杆件拉力测量装置并应用于实际拉力测量中,其拉力测量误差和确定系数分别达到0.11%和1,达到了实际测量要求,证明了该方法的有效性和可用性。
英文摘要:
      From the magneto elastic effect principle, it is known that diversity exists in the hysteresis loops of rode like ferromagnetic material under different tension. So it can be employed to develop an improved method for tension estimation of rode like structure. Firstly, the hysteresis loop signals of rod like structure are acquired by an EM sensor which is composed of two coaxial solenoid coils. Then, a curve of hysteresis loop change (CHLC) is defined to reflect the tension influence on each point of the hysteresis loop. Secondly, low resolution CHLC is obtained by using wavelet analysis. Finally, neural network is used to establish the relationship between the low resolution CHLC and tension after the data with different tension is used to train the neural network. The experimental results show that CHLC can reflect the tension influence on each point of hysteresis loop intuitively. The low resolution CHLC not only has the characteristics of including the entire information of tension but also has the characteristics of low dimensionality. The relationship between the low resolution CHLC and tension can be obtained by using neural network, without analysis of the sensitivity curve and coefficient of determination curve. The simple linear interpolation based RBF neural network has a better performance than BP neural network and RBF neural network. The tension measurement method based on low resolution CHLC and simple linear interpolation based RBF neural network is applied to two coaxial solenoid coils based EM sensor to measure the tension and its average prediction error and coefficient of determination are 0.11% and 1, respectively. This method is effective and can meet the actual measurement requirements.
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