提高光栅测量精度和分辨率的技术研究与实现

随着人工神经网络和数字处理技术的发展,从另一角度研究了一种不必增加光栅刻线,仅采用普通刻线的光栅(50刻线 mm),即可实现高精度、高分辨率的光栅测量技术,并用该技术设计了一个测量系统。系统以高速数字信号处理器(DSP)为核心硬件,用单输入单输出模糊小脑神经网络(SISOFCMAC)拟合出光栅信号,再利用FCMAC的泛化能力,实现对光栅信号的连续数字细分,提高光栅测量精度与分辨率。     

用神经网络技术实现的高精度光栅测量装置

描述了一种用神经网络技术实现的高精度光栅测量装置。该装置采用BP神经网络对光栅信号进行细分，在仅用7个训练样本的情况下，细分精度可达0.18μm，使装置的分辨率得到很大提高，同时，简化了硬件设计，提高了系统的可靠性。     

采用软件细分技术的光栅测角仪

本文叙述的光栅数显测角仪,采用计算机软件对光栅莫尔条纹信号实现了400细分,与用硬件细分莫尔条纹信号方法相比,该整机具有成本低廉、准确度高、显视直观、并可根据需要进行实时控制、操作简单等特点。     

不依赖于长度基准的光栅细分精度的评定方法

分析了光栅细分精度与利萨如图形的关系，给出了通过利萨如图形评定光栅细分精度的方法。该方法将对测量条件要求极高的细分误差评定工作转化为普通的电压测量和计算，使光栅细分精度的评定不再依赖于高精度的长度基准。该方法适用于各种误差模型的光栅信号，也适用于其它任何以相位差为９０度的两种信号进行细分计量仪器。     

非同步采样法的光栅纳米测量

光栅计量技术向纳米测量发展需要更高的光栅细分倍数.基于所研制的快速互补函数细分算法,细分精度即仅取决于测量启停时刻的光栅信号采样精度,选用市场上货源丰富的、低成本的扫描式多通道模数转换卡,即可实现光栅纳米测量所需的高倍数细分.在测量的启停时刻对光栅信号进行精确测量,在测量过程中对光栅信号进行快速跟踪测量.实验表明,当采用15位200 kHz的A/D转换卡对20 μm栅距的光栅信号进行非同步采样时,可以得到小于1 nm的测量分辨率及大于120 mm/s的测量速度.     

一种基于CNN-BiGRU孪生网络的轴承故障诊断方法

针对轴承故障样本稀缺，传统深度神经网络模型在小样本情况下容易出现过拟合现象，泛化性能不好的问题，提出一种基于CNN-BiGRU孪生网络的轴承故障诊断方法。孪生网络采用两个结构相同、权值共享的卷积神经网络和双向门控循环单元组成，构造相同类别和不同类别的轴承样本对输入孪生网络，通过计算轴承样本对之间的L1距离进行相似性度量，实现轴承故障诊断。与传统深度神经网络相比，孪生网络采用样本对训练的方法，在相同样本数量情况下，增加对网络模型的有效训练次数，从而提高轴承故障诊断性能。设计卷积神经网络和双向门控循环单元共同组成孪生网络结构，可以从振动信号中同时提取空间特征与时序特征，提高特征提取的准确性。利用实测轴承故障信号进行故障诊断试验，并与其他深度神经网络模型进行对比，试验结果表明，CNN-BiGRU孪生网络方法在少量训练样本情况下，取得了较优的故障诊断性能，有一定的工程应用价值。     

基于CPLD芯片的光栅信号细分模块的设计

针对光栅位移传感器信号设计了一种全新的硬件细分模块,在模拟细分基础上由一片CPLD芯片实现二次细分、辨向、计数、锁存和接口设计,可与计算机之间基于增强并行口EPP协议进行在线数据传输,该设计已经应用于大量程光纤绝对测距系统中.     

基于比值线性化的高适应性光栅细分方法研究

电子细分是光栅位移传感器实现纳米级分辨率测量的关键,细分误差由细分方法和光栅信号质量共同决定。针对信号实时修正方法在小步距测量和一些复杂工况环境下的局限性,从提高细分方法对非理想光栅信号的适应性的角度出发,提出了基于信号比值线性化的新型电子细分方法,构建了2种实时补偿信号以进一步提高信号的线性度;详细阐述了所提细分方法的细分原理,分析了其在非理想光栅信号输入情况下的细分误差;实现了最高为0.003μm的理论细分精度和0.08μm的实际细分精度。数值仿真和对比实验结果表明,所提细分方法对非理想光栅信号的适应性明显优于常用的反正切细分法和正余弦绝对值相减细分法。     

机器人柔顺装配夹具的双小脑模型神经网络控制系统

介绍了一种新型的机器人柔顺装配夹具及其装配系统。设计了由装配力信号为输入的小脑模型神经网络CMAC1和以浮动平台位移信号为输入的小脑模型神经网络CMAC2并行叠加构成的双CMAC神经网络控制器，前者作为前馈控制，后者当夹具刚启动时为前馈控制，装配开始后转为反馈控制。双CMAC神经网络控制器具有存储容量少、系统较稳定、能减少系统误差影响、装配力小等优点。     

基于时间序列分析的光栅信号时域细分方法（英文）

针对光栅细分倍数和精度受到莫尔条纹信号质量限制的问题,提出一种基于时间序列分析的光栅信号时域细分方法.首先利用等空间间隔的栅距与采样时间周期的对应关系,将空间栅距转换为时间序列.然后利用时间序列分析中的自回归模型进行建模预测,并采用最大似然估计法计算模型参数.最后在预测时间内发送出代表位移的细分脉冲.实验表明,细分倍数为400或800时的角位移细分误差均为±2.4″,且光栅运动加速度及其变化率越小,细分误差越小.此细分方法一定程度上降低了对光栅信号质量的依赖,具有一定的应用价值.     

大量程纳米级光栅干涉位移测量

针对传统光栅干涉仪中测量范围和分辨率难以同时提高的问题,提出利用单根大长度、低线数光栅实现大量程、高分辨率位移测量的方法.首先利用长度400mm,栅距10μm计量光栅的±5级衍射光生成条纹图,实现了条纹的10倍光学细分.然后提出一种基于傅里叶变换时移特性的条纹细分新方法,利用相邻两帧条纹图同一位置处相位的变化实现了高达1000倍的条纹电子细分.在此过程中,针对能量泄漏对傅里叶变换法相位提取精度的影响,提出条纹图整周期裁剪的方法,使条纹细分精度至少可达到1/1000条纹周期.仿真和实验结果表明,系统具有纳米级的分辨率和优于10nm的测量精度.     

高精度数字光电自准直仪

本文介绍一种采用微机和光栅技术的高精度数字光电自准直仪的性能指标、结构原理和电气原理,阐述了在该仪器中成功应用的软件细分方法,实现了光电自准直仪的升级换代。     

High-capacity neural networks on nonideal hardware

We present a new training-out algorithm for neural networks that permits good performance on nonideal hardware with limited analog neuron and weight accuracy. Optical neural networks are emphasized with the error sources including nonuniform beam illumination and nonlinear device characteristics. We compensate for processor nonidealities during gated learning (off-line training); thus our algorithm does not require real-time neural networks with adaptive weights. This permits use of high-accuracy nonadaptive weights and reduced hardware complexity. The specific neural network we consider is the Ho-Kashyap associative processor because it provides the largest storage capacity. Simulation results and optical laboratory data are provided. The storage measure we use is the ratio M/N of the number of vectors stored (M) to the dimensionality of the vectors stored (N). We show a storage capacity of M/N = 1.5 on our optical laboratory system with excellent recall accuracy, > 95%. The theoretical maximum storage is M/N = 2 (as N approaches infinity), and thus the storage and performance we demonstrate are impressive considering the processor nonidealities we present. Our techniques can be applied to other neural network algorithms and other nonideal processing hardware.     

基于改进判别字典学习的故障诊断方法

近年来,基于稀疏表示的分类技术在模式识别中取得一定的成功。该框架中,字典的学习和分类器的训练通常是两个独立的模块,降低了方法的识别精度。针对以上问题,提出了一种特征提取和模式识别相融合的改进判别字典学习模型,将重构误差项、稀疏编码判别项及分类误差项进行了整合,并用K奇异值分解算法对目标函数进行优化,实现了字典和分类器的同步学习。该方法先对原始信号进行经验模态分解,并从分解的本征模态函数中提取时、频特征,形成故障样本;然后将训练样本输入改进模型用K奇异值分解优化;最后用习得字典及分类器权重对测试样本进行识别。实验结果表明：该算法不但适用于小样本故障问题,而且鲁棒性和分类性能都明显高于其它算法。         

Quantitative analysis of low-field NMR signals in the time domain

Two novel methods are described for direct quantitative analysis of NMR free induction decay (FID) signals. The methods use adaptations of the generalized rank annihilation method (GRAM) and the direct exponential curve resolution algorithm (DECRA). With FID-GRAM, the Hankel matrix of the sample signal is compared with that of a reference mixture to obtain quantitative data about the components. With FID-DECRA, a single-sample FID matrix is split into two matrices, allowing quantitative recovery of decay constants and the individual signals in the FID. Inaccurate results were obtained with FID-GRAM when there were differences between the frequency or transverse relaxation time of signals for the reference and test samples. This problem does not arise with FID-DECRA, because comparison with a reference signal is unnecessary. Application of FID-DECRA to 19F NMR data, which contained overlapping signals from three components, gave concentrations comparable to those derived from partial least squares (PLS) analysis of the Fourier transformed spectra. However, the main advantage of FID-DECRA was that accurate (<5% error) and precise (2.3% RSD) results were obtained using only one calibration sample, whereas with PLS, a training set of 10 standard mixtures was used to give comparable accuracy and precision.     

十字光栅多普勒效应二维测量系统的研究

提出一种利用十字光栅衍射的多普勒效应进行二维位移测量的新方法。给出了以二维全息光栅为多普勒频移发生器、以矩形槽相位光栅为混频器的光路结构和测量原理。理论计算和实验表明,此系统具有良好的信号质量和较高的测量精确度,奇用于生产环境中的两坐标精密检测。     

Phase-modulation scatterometry

Phase-modulation scatterometry is a metrology technique for determining, by means of a phase modulator as a key device, the parameters of gratings. The main source of error to be dealt with are the fluctuations of the phase-modulation amplitude. The grating zeroth-order reflectance modulated by the phase modulator is converted into a signal by the photodetector. The measurables are the direct term and the first two harmonics of the signal. For experimental data fitting, we used the ratio of the harmonics over the direct term because it significantly improves the accuracy. A sensitivity analysis was performed for two samples, one real and one theoretical, to find the measurement configuration that insures optimum determination precision for the grating parameters. For the real sample, comparison of the theoretical predictions for sensitivity with the actual values showed a good agreement. For both samples the sensitivity analysis indicated subnanometric precision for the critical dimension (grating linewidth).