基于ECT的航空复合材料缺陷检测研究

针对航空复合材料制件在生产和服役过程中产生的缺陷损伤,提出了一种基于电容敏感原理的同面多电极阵列传感器无损检测方法。基于电容边缘效应,设计了一套同面电极阵列的胶粘缺陷检测系统,模拟航空复合材料缺陷的真实分布。电容层析成像(ECT)系统以单电极激励方式工作,根据缺陷材料介电特性变化引起的电容测量值的变化,采用共轭梯度成像算法复现缺陷位置和形状。实验结果表明,所述方法能够准确地判断出胶粘缺陷的位置和大小,能够对2mm厚度碳纤维复合材料内侧的8mm胶粘结构缺陷进行可视化检测,ECT检测系统重建图像质量较高、边缘伪影较少,为航空复合材料缺陷检测提供了一种新的研究方向。     

三维电容层析成像组合电极激励测量模式

针对3层电容阵列电极传感器,基于三维仿真,研究了两种组合电极激励测量模式,并与传统的单电极激励单电极测量模式进行了比较,包括电容测量值大小及其动态范围、敏感场分布均匀性以及重建图像质量。基于COMSOL软件及Matlab软件进行了仿真实验,结果表明:相较于传统的单电极激励单电极测量模式,组合电极激励测量模式所得图像重建效果更好,测量电容值较大且动态范围较小,敏感场分布更加均匀,对中心物体的成像质量明显提高。     

基于均匀设计及非线性偏最小二乘法的非闭合电极ECT传感器参数优化设计

提出了一种非闭合电极电容层析成像(ECT)传感器结构参数的优化方法.采用均匀设计结合非线性偏最小二乘(NPLS)回归,提取传感器结构参数(电极极板的宽度 L、绝缘外壳的壁厚δ1.、屏蔽罩与绝缘外壳间距δ2及绝缘外壳材料的相对介电常数ε)与待优化指标(敏感场的均匀度及灵敏度指标p1、最大与最小电容的比值 K)间的函数关系,建立相应的优化目标泛函,通过对优化目标泛函的求解,最终获得传感器结构参数的最优值.并以 10 电极非闭合电极 ECT 传感器为研究对象,进行了结构参数的优化设计,根据优化结果设计制作了非闭合电极 ECT 传感器,对其成像进行了仿真与实测.结果表明,参数优化后的传感器图像重建质量优于未优化的传感器.     

一种电容层析成像系统电极组合激励测量方法

针对电容层析成像图像重建具有非线性不适定性的问题,提出了8组24电极旋转激励测量方案,并与传统12电极电容层析成像传感器进行了对比分析,包括电容值大小及其动态范围、敏感场分布均匀性以及重建图像质量。应用COMSOL软件及Matlab软件对两种传感器激励模式进行仿真,结果表明:所提出的8组电极旋转激励测量模式,其电容独立测量数与传统12电极传感器相近,可较好地保证实时成像速度,测量电容值较大且动态范围较小,降低了信号检测电路设计难度,测量区域灵敏场均匀性有所改善,对中心物体的成像质量明显提高。     

用于气固两相流在线测量的ECT电容测量电路

电容层析成像(ECT)技术是目前最具发展前景的多相流参数检测方法.介绍了12电极ECT成像系统的测量原理及设计方法.研制了一种交流激励的微电容测量电路,该电路具有较强的抗杂散电容性能、结构简单、容易实现等优点,较好地解决了电子开关的电荷注入效应对测量分辨力的影响问题.经试验证明可达到的实际分辨力为0.04fF.     

双电极联合循环工作模式下阵列电极的传感能力分析

同面阵列电极主要基于边缘电场工作使其敏感场分布呈现软场特性,其传感能力易受外部多因素影响而降低。为了提高同面电容传感器的检测能力和检测效率,本文针对3×4同面阵列电极,提出了一种两极板联合循环激励的测量模式。通过对同面电极电势分布的有限元仿真,分析了该激励模式下的电场穿透深度。求解了阵列电极的灵敏度矩阵,根据其数据离散系数评判敏感场的均匀性。通过建立3种不同被测物场的模型,分别分析其电容数据的动态范围,并进行图像重建,采用图像相关系数比较图像质量的提升效果。结果表明,在联合循环电极激励测量模式下,阵列电极穿透深度和敏感场分布均匀性均好于单电极激励模式,重建图像更清晰地反映了待测物体的实际形态和轮廓,综合验证了阵列电极传感能力得以提高。     

基于ANSYS的同面多电极电容传感器仿真研究

同面多电极电容传感器是一种新的飞机复合材料构件损伤检测技术,该检测技术的正问题中敏感场分布函数的计算要涉及到静电场中电容计算问题。在仿真研究中,电容要通过场的数值计算方法来求得。本文介绍了如何使用ANSYS有限元分析计算同面多电极电容传感器中的各电极对之间的电容,对检测材料发生异常及传感器参数改变时电容的变化进行了分析,为损伤检测的特征提取和逆问题图像重构提供了仿真数据。     

方形电容层析成象系统的仿真研究

本利用自行开发的软平台,引入方形管道电容成象系统的模型,用限元法计算了敏感场的分布,并结合多种图象重建算法针对１２电极方形ＥＣＴ系统进行了仿真实验,得到了令人满意的结果。     

基于SVM的ECT图像重建算法

电容层析成像（ECT）技术是基于电容敏感机理的过程层析成像技术。ECT的图像重建是一个典型的有限样本非线性映射问题。支持向量机（SVM）作为一种小样本处理方法,具有较强的泛化能力,被认为是目前针对小样本分类问题的最佳理论。提出了一种基于SVM的四层神经网络的图像重建算法,仿真结果表明,该算法用于三相流图像重建具有较强的空间分辨率和泛化能力。     

低温流体电容层析成像空间分辨率研究

为了深入探究电容层析成像(ECT)在用于低温流体(液汽介电常数比比室温介质小1个数量级以上)的两相流反演成像时的成像性能,基于数值模拟,对比确认最佳成像方法后,通过对比图像质量和成像定量指标研究了低温流体在4种两相流型下的成像空间分辨率。结果表明成像分辨率在测量中心区域为直径比0.2,边缘区域为0.1,影响因素包括主流体相对介电常数、两相介质的介电常数比、数据噪声等,其中分辨率对测量噪声尤为敏感。研究定量验证了ECT用于低温流体测量时的分辨率性能,并为未来传感器的优化指明了方向。     

A Multimodal Tomography System Based on ECT Sensors

A new noninvasive system for multimodal electrical tomography based on electrical capacitance tomography (ECT) sensor hardware is proposed. Quasistatic electromagnetic fields are produced by ECT sensors and used for interrogating the sensing domain. The new system is noninvasive and based on capacitance measurements for permittivity and power balance measurements for conductivity (impedance) imaging. A dual sensitivity map of perturbations in permittivity and conductivity is constructed. The measured data along with the sensitivity matrix are used for the actual image reconstruction. The new multimodal tomography system has the advantage of using already established reconstruction techniques, and the potential for combination with new reconstruction techniques by taking advantage of combined conductivity/permittivity data. Moreover, it does not require direct contact between the sensor and the region of interest. The system performance has been tested on representative data, producing good results     

Image reconstruction algorithm based on the extended regularised total least squares method for electrical capacitance tomography

Electrical capacitance tomography (ECT) is a non-invasive imaging technology that aims at the visualisation of the cross-sectional permittivity distribution of a dielectric object based on the measured capacitance data. Successful applications of ECT depend greatly on the precision and speed of the image reconstruction algorithms. ECT image reconstruction is a typical ill-posed problem, and its solution is unstable, that is, the solution is sensitive to noises in the input data. Methods that ensure the stability of a solution while enhancing the quality of the reconstructed images should be used to obtain a meaningful reconstruction result. An image reconstruction algorithm based on the regularised total least squares (TLS) method that considers the errors in both the sensitivity field matrix and the capacitance data for ECT is presented. The regularised TLS method is extended using a combination robust estimation technique and an extended stabilising functional according to the ill-posed characteristics of ECT, which transforms the image reconstruction problem into an optimisation problem. In addition, the Newton algorithm is employed to solve the objective functional. Numerical simulations indicate that the algorithm is feasible and overcomes the numerical instability of ECT image reconstruction; for the cases of the reconstructed objects considered here, the spatial resolution of the reconstructed images obtained using the algorithm is enhanced; as a result, an efficient method for ECT image reconstruction is introduced.     

Electrical Capacitance Volume Tomography

A dynamic volume imaging based on the principle of electrical capacitance tomography (ECT), namely, electrical capacitance volume tomography (ECVT), has been developed in this study. The technique generates, from the measured capacitance, a whole volumetric image of the region enclosed by the geometrically three-dimensional capacitance sensor. This development enables a real-time, 3-D imaging of a moving object or a real-time volume imaging (4-D) to be realized. Moreover, it allows total interrogation of the whole volume within the domain (vessel or conduit) of an arbitrary shape or geometry. The development of the ECVT imaging technique primarily encloses the 3-D capacitance sensor design and the volume image reconstruction technique. The electrical field variation in three-dimensional space forms a basis for volume imaging through different shapes and configurations of ECT sensor electrodes. The image reconstruction scheme is established by implementing the neural-network multicriterion optimization image reconstruction (NN-MOIRT), developed earlier by the authors for the 2-D ECT. The image reconstruction technique is modified by introducing into the algorithm a 3-D sensitivity matrix to replace the 2-D sensitivity matrix in conventional 2-D ECT, and providing additional network constraints including 3-to-2-D image matching function. The additional constraints further enhance the accuracy of the image reconstruction algorithm. The technique has been successfully verified over actual objects in the experimental conditions     

电容层析成像三维图像重建研究

进行了电容层析成像(ECT)三维图像重建的仿真研究。首先利用COMSOL软件对ECT传感器进行三维建模,基于其高精度的有限元求解,计算ECT的灵敏度矩阵;其次,基于Matlab软件实现了基于线性反投影(LBP)及Landweber迭代算法的ECT图像重建,并利用图像显示程序获得了三维重建图像;最后,对球体及圆柱体模型进行了三维ECT图像重建,获得了较好的重建结果。     

油水两相流浓度电容层析成像的测量方法

以8电极油水两相流电容层析成像系统为研究对象,分析了油水两相流电容层析成像系统的测量原理,依据二重积分微元原理建立了油水两相流相浓度分布信息的数学模型,采用ART图像重建的方法完成油水两相流的浓度测量,并在重建算法中引入了阈值以提高图像重建质量和含率计算的准确性,测量误差在1%左右,较好地解决了其他测量方法速度慢,成本高,安全性差等缺陷,为油水两相流浓度的测量提供了一种新方法.     

Dual imaging modality of granular flow based on ECT sensors

In this study, a previously developed dual modality imaging system is applied to image the flow of granular matter with different electrical properties in cylindrical vessels. The imaging system is based on both capacitance and power measurements acquired by an electrical capacitance tomography (ECT) sensor located around the vessel. The measurement data are then used to reconstruct cross-sectional images of both permittivity and conductivity distributions. A neural network multi-criterion optimization reconstruction technique (NN-MOIRT) is used for the inverse (reconstruction) problem. The contribution of this technology to the field of granular matters is explored through review of research articles that can be a direct application of this development. We discuss the capabilities of this dual-modality acquisition system using synthetic data for granular matter with different electrical properties.     

基于深度信念网络的ECT图像重建算法

为提高图像重建质量,针对电容层析成像技术(ECT)中的电容数据复杂多样且与介电常数呈非线性关系的特点,提出一种基于深度信念网络(DBN)的重建算法,利用DBN的深层非线性网络结构来实现电容值与重建图像灰度值非线性关系.并对DBN进行了改进,将自适应步长(AS)引入到对比散度(CD)算法中,解决固定步长寻找全局最优困难的...     

Helmholtz-Type Regularization Method for Permittivity Reconstruction Using Experimental Phantom Data of Electrical Capacitance Tomography

Electrical capacitance tomography (ECT) attempts to image the permittivity distribution of an object by measuring the electrical capacitance between sets of electrodes placed around its periphery. Image reconstruction in ECT is a nonlinear ill-posed inverse problem, and regularization methods are needed to stabilize this inverse problem. The reconstruction of complex shapes (sharp edges) and absolute permittivity values is a more difficult task in ECT, and the commonly used regularization methods in Tikhonov minimization are unable to solve these problems. In the standard Tikhonov regularization method, the regularization matrix has a Laplacian-type structure, which encourages smoothing reconstruction. A Helmholtz-type regularization scheme has been implemented to solve the inverse problem with complicated-shape objects and the absolute permittivity values. The Helmholtz-type regularization has a wavelike property and encourages variations of permittivity. The results from experimental data demonstrate the advantage of the Helmholtz-type regularization for recovering sharp edges over the popular Laplacian-type regularization in the framework of Tikhonov minimization. Furthermore, this paper presents examples of the reconstructed absolute value permittivity map in ECT using experimental phantom data.