冲击波压力传感器动态特性补偿及其应用（英文）

为修正压力传感器动态特性引起的测试误差,避免传感器动态建模误差影响补偿结果,提出了一种基于量子粒子群优化(QPSO)算法和均方误差的传感器动态补偿方法。通过对传感器进行逆建模,寻优得到了最优阶次的补偿器系数,利用激波管动态校准实验对该方法进行了验证,分析了补偿前后传感器的时域与频域特性。结果表明,该方法有效扩展了传感器的工作频带;在实弹测试中,减小了动态测量误差,提高了测试精度。     

传感器动态性能的研究进展

传感器的动态特性直接影响测试系统功能的发挥,国内外对传感器动态性能进行了大量的研究,回顾了传感器辨识建模和动态补偿等的研究进展与成果,并分析了传感器动态特性的研究前景.     

应用递推神经网络的传感器动态建模研究

根据动态校准实验结果建立传感器的动态数学模型 ,以研究传感器的动态性能 ,是动态测试的一个重要内容。研究了递归神经网络模型在传感器动态建模中的应用。递归神经网络模型采用具有输入层、中间层、输出层的三层网络结构 ,整个网络的特性决定于相邻层间的连接权。采用递推预报误差算法训练神经网络 ,具有收敛速度快、收敛精度高的特点。由于其反馈特征 ,使得递归神经网络模型能获取系统的动态响应特性。该方法特别适用于传感器非线性动态建模 ,而且避免了传感器模型阶次的选择的困难。试验结果表明 ,应用递归神经网络对传感器进行动态建模是一种行之有效的方法     

时栅传感器动态测量误差补偿

针对动态测量误差特点,提出了对系统误差和随机误差分别进行建模和组合补偿的思想来提高时栅传感器的动态测量精度。对具有周期性变化特征的系统误差采用傅里叶级数逼近的方法进行建模,运用最小二乘求解超定方程组的方法计算出系统误差的补偿参数。对于系统误差补偿后残留的随机误差采用灰色预测GM(1,1)模型进行预测,通过模型残差检验和修正提高预测的准确度。实验结果表明,利用傅里叶级数逼近模型有效地补偿了系统误差,误差由±35″降至±7.8″,通过最小二乘参数寻优得到的补偿参数与传感器实际的误差成分相吻合;灰色预测模型则很好地预测补偿了残留的随机误差,误差由±7.8″降至±3″。得到的结果表明,利用这种对误差分别建模和补偿的方法大幅度地降低了动态测量误差,有效地提高了传感器的测量精度。     

时栅动态测量误差建模与补偿技术研究

为提高时栅传感器动态测量精度,针对时栅的误差特点,提出对动态测量误差的周期性成分和随机性成分分别建模的思想。采用傅里叶级数逼近的方法对误差中的周期性成分进行建模,利用最小二乘方法对逼近模型参数进行寻优,选取比重较大的谐波参数对误差的周期性成分进行分离,对于分离后残留的随机性成分采用支持向量回归(Support vector regression,SVR)模型进行预测,利用交叉验证的方法对回归预测模型进行参数寻优和细化,选取最优的核函数参数g和惩罚因子C,使得残差均方达到最小。研发误差补偿系统,对时栅动态测量误差进行补偿。试验结果表明,运用该建模方法和模型,时栅传感器动态测量误差的峰峰值由38.2″降至3″,有效地降低了测量误差,大幅度提高了传感器的测量精度。     

DP传感器研究及桥梁自振特性测试

研究了磁电传感器的动态特性补偿方法，开发出具有高机械稳定性和低固有频率的ＤＰ传感器，并利用ＤＰ传感器对铁路桥梁的自振特性进行了测试。实验表明了该传感器的有效性。     

振动测试用传感器低频特性分析与补偿

针对低频振动环境对航天产品结构的影响和危害,提出了基于正弦压力动态系统的一种振动测试用压电传感器低频动态研究方法,该方法应用双向双通道进出口同步技术的正弦压力动态系统对PCB公司的某压电传感器低频特性进行了实验研究,得到其下限截止频率为0.26 Hz。研究发现,当被测振动或冲击信号频率在1～200 kHz时,信号的正压作用时间的误差范围为4.87%～0.03%。并针对测振传感器低频响应进行了补偿,补偿效果良好。     

基于预测误差法的加速度传感器动态模型参数辨识

加速度传感器动态模型对研究与分析加速度传感器的动态特性与动态误差补偿具有重要作用。针对加速度传感器动态模型的参数辨识,提出了一种基于预测误差法的加速度传感器动态模型参数辨识方法,该方法将加速度传感器的状态空间模型转化为线性带外生输入的自回归滑动平均(ARMAX)模型,获得其最优一步预测输出的表达式,并通过求解加速度传感器最优一步预测输出极小化误差准则函数,实现加速度传感器动态模型参数的最优辨识。实验结果表明,该方法有效地实现了加速度传感器动态模型的参数辨识,所得加速度传感器动态模型具有较高的精度,能描述加速度传感器的动态特性。     

压力传感器加速度效应的系统辨识与建模研究

压力场测试过程中,由于压电型压力传感器的加速度效应给测试带来很大误差。针对这一问题,对压电型压力传感器中压电材料的加速度效应进行了理论分析和有限元静态与动态研究分析。根据系统辨识理论,运用马歇特锤试验数据,对压力传感器加速度效应进行了ARX模型系统建模。基于该模型建立加速度效应数字补偿滤波器,试验结果表明:经过该模型滤波后压力传感器加速度效应降低88%以上。该模型具有精确度高,可靠性和通用性强的特点。     

基于多元回归算法的激光位移传感器非线性误差建模和补偿

本文针对三角测量法激光位移传感器的非线性误差产生原理进行了分析,并提出了误差建模算法。通过建模可实现误差的实时估计,并代入传感器测量值中对误差进行补偿,从而达到提升传感器测量精度的目的。提出了对应的误差测量方法,并对误差进行了实验测量和建模,对模型补偿效果进行了验证,结果表明,通过补偿能够大幅提升传感器的检测精度。     

Correction of sensor's dynamic error caused by system limitations

The method based on particle swarm optimization (PSO) integrated with functional link articial neural network (FLANN) for correcting dynamic characteristics of sensor is used to reduce sensor's dynamic...     

Thermal error analysis,modeling and compensation of five-axis machine tools

The role of five-axis CNC machine tools (FAMT) in the manufacturing industry is becoming more and more important, but due to the large number of heat sources of FAMT, the thermal error caused by them will be more complicated. To simplify the complicated thermal error model, this paper presents a new modelling method for compensation of the thermal errors on a cradle-type FAMT. This method uses artificial neural network (ANN) and shark smell optimization (SSO) algorithm to evaluate the performance of FAMT, and developing the thermal error compensation system, the compensation model is verified by machining experiments. Generally, the thermal sensitive point screening is performed by a method in which a large number of temperature sensors are arranged randomly, it increases the workload and may cause omission of the heat sensitive point. In this paper, the thermal imager is used to screen out the temperature sensitive points of the machine tool (MT), then the temperature sensor is placed at the position of the heat sensitive point of the FAMT, and the collected thermal characteristic data is used for thermal error modeling. The C-axis heating test, spindle heating test, and the combined movement test are applied in this work, and the results show that the shark smell optimization artificial neural network (SSO-ANN) model was compared to the other two models and verified better performance than back propagation artificial neural network (BP-ANN) model and particle swarm optimization neural network (PSO) model with the same training samples. Finally, a compensation experiment is carried out. The compensation values, which was calculated by the SSO-ANN model are sent to the real-time error compensation controller. The compensation effect of the model is then tested by machining the ‘S’-shaped test piece. Test results show that the 32 % reduction in machining error is achieved after compensation, which means this method improves the accuracy and robustness of the thermal error compensation system.

     

Auto-calibration and -compensation of a capacitive pressure sensor using multilayer perceptrons

Using multilayer perceptrons (MLPs), a smart model for a capacitive pressure sensor (CPS) is proposed. When the ambient temperature changes, the nonlinear response characteristics of a CPS may vary widely. Under such conditions, calibration of the sensor and compensation of the nonlinear sensor characteristics to obtain correct readout becomes a difficult task. The proposed MLP model can provide automatic nonlinear compensation and calibration of the CPS characteristics. A microcontroller unit (MCU)-based implementation scheme for this model is also considered. Simulation results show that this model can estimate the pressure with a maximum full-scale error of +/- 1% over a variation of temperature from -50 to 150 degrees C.     

Floating clamping mechanism of PT fuel injector and its dynamic characteristics analysis

PT fuel injector is one of the most important parts of modern diesel engine.To satisfy the requirements of the rapid and accurate test of PT fuel injector,the self-adaptive floating clamping mechanism was developed and used in the relevant bench.Its dynamic characteristics directly influence the test efficiency and accuracy.However,due to its special structure and complex oil pressure signal,related documents for evaluating dynamic characteristics of this mechanism are lack and some dynamic characteristics of this mechanism can’t be extracted and recognized effectively by traditional methods.Aiming at the problem above-mentioned,a new method based on Hilbert-Huang transform(HHT) is presented.Firstly,combining with the actual working process,the dynamic liquid pressure signal of the mechanism is acquired.By analyzing the pressure fluctuation during the whole working process in time domain,oil leakage and hydraulic shock in the clamping chamber are discovered.Secondly,owing to the nonlinearity and nonstationarity of pressure signal,empirical mode decomposition is used,and the signal is decomposed and reconstructed into forced vibration,free vibration and noise.By analyzing forced vibration in the time domain,machining error and installation error of cam are revealed.Finally,free vibration component is analyzed in time-frequency domain with HHT,the traits of free vibration in the time-frequency domain are revealed.Compared with traditional methods,Hilbert spectrum has higher time-frequency resolutions and higher credibility.The improved mechanism based on the above analyses can guarantee the test accuracy of injector injection.This new method based on the analyses of the pressure signal and combined with HHT can provide scientific basis for evaluation,design improvement of the mechanism,and give references for dynamic characteristics analysis of the hydraulic system in the interrelated fields.     

面阵彩色航空遥感相机前向像移补偿机构精度分析

考虑航空遥感相机中前向像移补偿机构的运动精度会直接影响相机分辨率,本文对相移补偿机构的补偿精度进行了分析。首先,研究了面阵彩色航空遥感相机的机械式前向像移补偿机构的特点,给出了对应于曝光时间的前向像移补偿速度残差的许用值;其次,分析了影响像移补偿精度的主要误差来源,建立了速度补偿残差的数学模型,并运用MonteCarlo算法对其进行了仿真分析;最后,通过动态目标发生器成像试验对仿真结果进行验证。试验结果显示,前向像移补偿机构的速度补偿残差3σ为395.6μm/s,与仿真分析结果400μm/s基本一致,说明该精度分析方法可以比较准确地确定前向像移补偿精度。     

嵌入式时栅角位移传感器短周期误差分析与补偿

为提高嵌入式时栅角位移传感器测量精度,从传感信号形成机理出发,对短周期误差成因进行了详细分析。通过对绕组等效分析和激励信号分析,确定了短周期误差的主要特性为一次和二次误差,一次误差来源为零点残余误差和直流分量误差,二次误差来源为激励信号正交误差。针对短周期误差补偿,提出了基于超限学习机的误差补偿方法,通过对测量值与真实值样本的训练得到模型最优参数,根据模型参数建立短周期误差模型,利用所得误差模型实现对短周期误差的补偿。实验结果表明,短周期误差分析结果与传感器实际误差特性一致,采用该补偿方法传感器短周期误差大幅度降低,降低了约96%。对比和重复性实验表明,该方法与谐波补偿法相比精度提高了约1倍,误差补偿效果更优,同时方法具有良好的测量稳定性,对提高嵌入式时栅角位移传感器的测量精度具有重要的理论和现实意义。     

环状流波状液膜时平均厚度动态校准方法

为实现环状气液两相流界面波液膜厚度的准确测量,基于环状流界面波的理论分析和实验研究,将实时海浪模拟思想引入界面扰动波研究,建立了基于三级海浪函数的界面扰动波模型。设计了一种基于直接测量法的可调节插入深度的波状液膜厚度测量传感器。提出了基于占空比算法的时平均液膜厚度动态校准方法。对测量传感器进行测量不确定度来源分析及评定,提出了系统误差补偿算法。在双闭环可调压中压湿气实验装置上对上述测量传感器进行了实验验证,结果表明:以三级海浪为模型进行研究在任意统计时间与任意插入深度增量条件下,94.44%的实验点的相对测量不确定度在2%以内;基于直接测量法实现液膜厚度复现并使用系统误差补偿算法,提高了测量精度。     

Dynamic compensation for sensors based on particle swarm optimization and realization on LabVIEW

以冲击波压力测试为背景,介绍了一种基于粒子群优化算法(PSO)的动态补偿数字滤波器的设计方法。对压力传感器进行动态校准实验和计算机仿真,根据传感器动态标定时的输入输出数据及参考模型,利用粒子群优化算法进行寻优,得到的全局最优值即为传感器动态补偿器的系数,并利用LabVIEW平台完成了动态补偿滤波器的设计。实验结果表明:经过补偿器处理后的信号与输入的被测信号有良好的一致性。     

主轴回转误差补偿机理和动力学模型研究

从补偿作用机理角度分析了主轴回转误差与加工系统之间的动态相互作用。在此基础上,着重从拉普拉斯频域和时间域分析了镗床主轴回转误差补偿切削系统的动力学特性。通过理论分析和仿真试验,得出有效补偿位移不仅受工件-主轴系统刚度和刀具刚度的影响,而且还受到切削参数,如切削重叠系数的影响。仿真和试验结果表明,SREC(主轴回转误差补偿)技术能有效地提高加工精度,在实际工程应用中是可行的。