Sensor calibration and compensation using artificial neural network

Artificial neural network (ANN) based inverse modeling technique is used for sensor response linearization. The choice of the order of the model and the number of the calibration points are important design parameters in this technique. An intensive study of the effect of the order of the model and number of calibration points on the lowest asymptotic root-mean-square (RMS) error has been reported in this paper. Starting from the initial value of the nonlinearity in the characteristics of a sensor and required RMS error, it is possible to quickly fix the order of the model and the number of calibration points required using results of this paper. The number of epochs needed to calibrate the sensor, and thereafter the epochs needed to recalibrate in event of sensitivity or offset drifts, are also presented to bring out the convergence time of the technique. More importantly, the advantages of the ANN technique over traditional regression based modeling are also discussed from the point of view of its advantage in hardware simplicity in microcontroller based implementation. Results presented in this paper would be of interest to instrumentation design engineers.     

基于遗传神经网络的机器人腕力传感器动态建模与补偿方法

介绍用于MotomamV3X机器人上的新型多维腕力传感器,比较遗传算法与人工神经网络的特点,将遗传算法的交叉和变异操作进行改进,提出一种融合改进遗传算法(Genetic algorithm, GA)的函数连接型人工神经网络(Functional link artificial neural network, FLANN),并将其用于所介绍的新型机器人腕力传感器动态建模与动态性能补偿中。介绍动态建模与动态补偿原理及改进遗传神经网络算法,给出该传感器的动态模型和动态补偿模型。该方法利用腕力传感器的动态标定数据,采用改进遗传神经网络搜索和优化模型参数,保留了遗传算法的全局搜索能力和FLANN结构简单,鲁棒性好,且具备自学习能力的特点,克服了FLANN容易陷入局部极小的缺陷,具有快的网络训练速度及高的动态建模精度。理论分析和试验结果都证实了所提出的动态建模与动态补偿方法的有效性。     

基于Stewart结构六维大力传感器的性能分析及结构优化

针对基于Stewart结构新型六维大力传感器在结构设计时存在诸多性能指标难以均衡的问题,对传感器的结构几何参数与其性能的关系进行了分析。依据基于Stewart结构六维大力传感器的测量原理,建立了传感器测量精度与一阶静力影响系数矩阵条件数的关系模型。通过分析力及力矩一阶静力影响系数矩阵和力及力矩柔度矩阵,揭示了传感器的各向同性指标、灵敏度及其各向同性指标与其结构参数之间的相互关系。并通过图谱优化方法和遗传算法对新型六维大力传感器的结构参数进行了优化。其分析结果将为六维大力传感器的结构设计提供有效的参考。     

基于RBF神经网络的湿度传感器建模方法

针对高分子湿度传感器的测量精度易受温度影响的问题，提出了湿度硬件测量电路，建立了高分子湿度传感器的数学模型。此硬件测量电路的湿度传感器选用IH3605，温度传感器选用AD590。此数学模型采用RBF神经网络，利用在线测量的温湿度信号值，应用带遗忘因子的梯度下降算法进行网络参数的调整，同时给出了建模步骤。实际结果表明，该模型的误差小于0．5％RH，具有较高的精度。     

Gene expression programming for automatic circuit model identification in impedance spectroscopy: Performance evaluation

Circuit/model identification is applied to impedance spectroscopy when there is no prior knowledge of the inner workings of the sensor or process under analysis. This paper presents a performance assessment of gene expression programming for automatic circuit model identification in impedance spectroscopy. The main objective of this work is to improve gene expression programming specific implementation details, with pre-embedding knowledge regarding circuit simplification rules, in order to improve its performance for circuit identification in impedance spectroscopy. Three different versions of gene expression programming are presented, discussed and analyzed. Insight is given into the inner workings of gene expression programming while highlighting the proposed changes in the three versions. The performance of the improved algorithm is analyzed through numerical simulated impedance data. It is further validated by the successful application to measurements of a real sensor along with a study of its performance on measured data.     

应用于钢轨检测的便携式涡流探伤仪的研制

钢轨损伤是影响铁路运输安全的主要因素,国内主要依靠巡道工人的日常检测,但现在还没有一种针对巡道工人使用的设备。研究设计了一种灵敏度高、便于携带且经济的仪器。基于涡流探伤的基本原理,采用正交试验方法设计、制作了一种精细的涡流传感器,用于提取微弱的裂纹信号,再由后台放大器放大,经过处理后在显示器上显示裂纹缺陷的相对大小,最后蜂鸣器报警,实现了防患于未然的目的。     

Isolability of faults in sensor fault diagnosis

A major concern with fault detection and isolation (FDI) methods is their robustness with respect to noise and modeling uncertainties. With this in mind, several approaches have been proposed to minimize the vulnerability of FDI methods to these uncertainties. But, apart from the algorithm used, there is a theoretical limit on the minimum effect of noise on detectability and isolability. This limit has been quantified in this paper for the problem of sensor fault diagnosis based on direct redundancies. In this study, first a geometric approach to sensor fault detection is proposed. The sensor fault is isolated based on the direction of residuals found from a residual generator. This residual generator can be constructed from an input-output or a Principal Component Analysis (PCA) based model. The simplicity of this technique, compared to the existing methods of sensor fault diagnosis, allows for more rational formulation of the isolability concepts in linear systems. Using this residual generator and the assumption of Gaussian noise, the effect of noise on isolability is studied, and the minimum magnitude of isolable fault in each sensor is found based on the distribution of noise in the measurement system. Finally, some numerical examples are presented to clarify this approach.     

Tool Wear Length Estimation with a Self-Learning Fuzzy Inference Algorithm in Finish Milling

The geometric accuracy and surface roughness are mainly affected by the flank wear at the minor cutting edge in finish machining. A genetic algorithm-based fuzzy estimator obtained by a fuzzy inference algorithm to evaluate the minor flank wear length in finish milling is introduced. The fuzzy inference rules are trained by genetic algorithms (GA) through practice. Fuzzy membership functions and rules are usually decided upon subjectively. In this paper, the performance of the fuzzy estimator may be improved if the fuzzy inference model is supplemented by a genetic-based learning mechanism. The features sensitive to minor flank wear are extracted from the dispersion analysis of a time series AR model of the feed directional acceleration of the spindle housing. Linguistic rules for fuzzy estimation are constructed using these features, and then fuzzy inferences are carried out with test data sets under various cutting conditions. The proposed system turns out to be effective for estimating minor flank wear length, and its mean error is less than 13%.     

A hybrid model using genetic algorithm and neural network for process parameters optimization in NC camshaft grinding

Camshaft grinding is more complex comparing with the ordinary cylindrical grinding. Since its quality is mostly influenced by more factors, how to select process parameters quickly and accurately becomes the key to improve its quality and processing efficiency. In this paper, a hybrid artificial neural network (ANN) and genetic algorithm (GA) model is proposed to optimize the process parameters. In this method, a BP neural network model is developed to map the complex nonlinear relationship between process parameters and processing requirements, and a GA is used in order to improve the accuracy and speed based on the ANN model. The results show that the hybrid ANN/GA model is an effective tool for the process parameters optimization in NC camshaft grinding.     

A novel distributed variational approximation method for density estimation in sensor networks

In this paper, a consensus filter based distributed variational Bayesian (CFBDVB) algorithm is developed for distributed density estimation. Sensor measurements are assumed to be statistically modeled by a finite mixture model for which the CFBDVB algorithm is used to estimate the parameters, including means, covariances and weights of components. This algorithm is based on three steps: (1) calculating local sufficient statistics at every node, (2) estimating a global sufficient statistics vector using a consensus filter, (3) updating parameters of the finite mixture model based on the global sufficient statistics vector. Scalability and robustness are two advantages of the proposed algorithm. Convergence of the CFBDVB algorithm is also proved using Robbins–Monro stochastic approximation method. Finally, to verify performance of CFBDVB algorithm, we perform several simulations of sensor networks. Simulation results are very promising.     

基于遗传算法的深沟球轴承优化设计研究

给出了基于遗传算法的深沟球轴承优化设计的方法。针对传统算法在深沟球轴承优化设计中全局寻优能力较差问题,以标准遗传算法（Genetic AlgorithmGA）的算法参数,采用了最优解保存策略,并运用罚函数法处理约束。为了降低摩擦阻力和减轻磨损,减少发热量,针对轴承发热变形的一个主要因素摩擦进行建模。     

气室结构触觉传感器的研究

为了解决机械手触觉传感问题,介绍了一种气室结构触觉传感器及信号处理系统,并分析了传感器的结构及敏感原理和相应的信号处理方法。传感系统采用金属弹性元件为敏感器件,传感区域充满气体,当有物体触及时,弹性片将产生变形,变形信号通过光导纤维传输至信号处理器,CCD光电转换器将光信号转换为电信号。处理系统分析判断触及物体的大小、位置。阐述了传感系统所具有的体积小、触点多、灵敏度高等优点,并提出了应用设计方案。对于机械手触觉传感领域的研究具有一定的参考价值。     

Detection of abrupt change and applications in sensor decalibration monitoring

The primary objective of fault detection is to detect abrupt undesirable changes in a process at an early stage. This early detection has a potential of preventing loss of production and equipment damage due to these undesirable changes, thus reducing process downtime. This paper details the implementation of some parametric fault detection techniques for sensor decalibration monitoring. A parametric fault detection approach that is handled in depth in this paper is the local approach. This approach developed by Benveniste, Basseville, and Moustakides [Benveniste, A., Basseville, M., and Moustakides, G., The asymptotic local approach to change detection and model validation. IEEE Trans. Autom. Control AC-32 (7), 583-592 (1987)] offers a computationally inexpensive way to attain the objective of monitoring changes in model parameters. However, the algorithm in its original formulation is not applicable to certain processes such as sensors. Therefore, the local approach is coupled with other estimation algorithms such as the input independent Kalman filter to derive a robust sensor decalibration monitoring algorithm. The proposed fault detection algorithm is applied to a pilot scale process for evaluation of its performance.     

基于正则化 AdaBound 的区间二型模糊 神经网络软测量建模

针对复杂化工过程中存在强非线性、多变量耦合、参数时变及大时滞等因素,导致监测变量软测量精度不高的问题,提 出了一种基于正则化 AdaBound 的区间二型模糊神经网络(RAIT2FNN) 软测量建模方法。 首先为了解决区间二型神经网络 (IT2FNN)结构难以确定的问题,提出了一种采用激励强度和相似度定义增长和删减指标的自组织产生规则的算法。 该算法利 用激励强度的大小决定是否产生规则,并根据相似度进行规则的删减从而确定了区间二型模糊神经网络的结构。 其次,本文提 出正则化和 AdaBound 相结合的算法对 RAIT2FNN 模型相关参数进行修正,使得不同参数具有有界的自适应学习速率。 最后将 RAIT2FNN 作为软测量模型应用于环己烷无催化氧化过程尾氧浓度预测问题中。 实验结果为测试时间为 0. 008 2,训练 RMSE 为 0. 018 2,测试 RMSE 为 0. 009 6,表明 RAIT2FNN 作为软测量模型具有预测及时且预测精度较高的优点。     

基于非线性GA算法的动态P模型的参数辨识与验证

针对现有的GMM-FBG电流传感器的磁滞非线性问题,提出了一种改进的动态Preisach磁滞模型。采用非线性遗传算法对改进动态Preisach磁滞数学模型进行参数辨识,提高了动态磁滞曲线的预测精度。运用改进动态Preisach模型对GMMFBG电流传感器进行建模及实验验证,实验及仿真结果表明该模型具有较好的预测性,预测误差在3.0%以内。经过磁滞补偿使得传感系统电流的测量灵敏度达到0.050 nm/A。     

Computer simulation of a piezoelectric angular rate sensor

The paper presents the finite element (FE) modeling of operation of a rotational motion sensor that uses a balanced oscillator (tuning fork) to sense the angular rate. The 3D FE model has been employed for the investigation of the dynamic properties of the sensor. The sensitivity functions have been obtained for adjusting the geometric parameters of the quartz element in order to achieve the desired values of natural frequencies. The performance of dynamic computations has been improved by truncating the dynamic contributions of higher modes of the vibrating structure.

Results are presented in terms of performance characteristics of the sensor against the design parameters in various modes of operation.     

压电传感器在超声振动系统中的设计与应用

将压电原理用于实现超声振动系统的频率自动跟踪，在振动系统设计中加入压电传感片，压电传感片跟随系统振动，并产生感应电压，谐振时，感应电压达到最大值，搜寻感应电压最大值就能实现频率自动跟踪，压电传感器的设计可以采用四端网络法，该方法简单实用，为达到最佳灵敏度，应该尽可能让压电传感器靠近振动系统节面位置，压电传感器设计简单，实现容易，跟踪精度高，并且稳定可靠，还能同时用于实现对刀具有安全保护功能。     

葡萄糖传感器的实时自调整校准算法研究

针对皮下植入式葡萄糖传感器在实际动态监测过程中易受生理环境影响限制测量精度的问题,提出了一种基于单点校准技术优化的实时自调整校准算法实现传感器的信号漂移补偿和灵敏度衰减校正。该方法能够对单点校准过程中的关键参数(灵敏度和电流信号)进行调整从而建立最优模型。用最小二乘方法拟合出模型的初始参数,根据建立的最优模型对在体监测3 d的电流数据进行校准,结果表明,经该方法校准的血糖浓度相对误差在0.01%~6.67%范围内,优于传统单点校准模型的6.36%~16.92%,具有更高的校准精度和实用价值,有效提高了动态血糖监测的准确性。     

Applying the noising method to find the best regression model

Regression analysis is one of the most applicable methods in statistical methodology used to find the best regression model according to the relationship among several variables in a system. The estimation of regression model, which is solved as a formulate optimization problem and making use of heuristic algorithms, is much simpler and faster than classic methods. Genetic algorithm (GA) as one of the heuristic algorithms had been used to solve this problem. In this paper, we extend the noising method as a recent combinatorial optimization problem to estimate the best regression model and evaluate its performances compared to GA. Also, in order to enhance the performance of our GA, we apply the Taguchi experimental design method to tune the parameters of the algorithm.     

WO3对O3敏感特性研究

】实验发现ＷＯ３半导体材料对Ｏ３有好的敏感特性,而且元件的烧结温度和工作温度对其敏感特性有大的影响。在对这些参数优化的基础上,研制出了具有较好特性的Ｏ３气敏元件。测定了元件灵敏度与Ｏ３浓度的关系,初步讨论了元件对Ｏ３气体敏感的机制。