油管壁厚测量数据的一致性加权融合估计算法

在随机拔动环境中采用漏磁场法在线测量油管壁厚时，每个磁性传感器只能得到环境的部分信息。为了对各传感器的局部测量值进行稳健融合估计，提出了油管壁厚测量数据的一致性加权融合估计算法。利用改进的数据探测技术中的分布图方法对各个传感器的一致性进行检验，并采用简单加权平均法求得一致性传感器测量数据的稳健融合估计值。融合实例表明，该算法能有效地提高系统测量的稳健性，且具有精度高、运算简单等特点。该算法适用于多传感器测量数据的实时高精度融合估计。     

Use of design of experiments and Monte Carlo method for instruments optimal design

A common issue in the design of measurement instruments is the comparison between different solutions in terms of components of the measurement chain, data processing or even measurement principles; the predicted instrumental uncertainty is the driving parameter for such a comparison. While in many situations the linearization of the measuring model allows using the standard ISO GUM procedure, in complex cases it might be necessary to proceed with Monte Carlo simulations as per ISO GUM supplement 1. This paper describes a method that combines the factorial design of experiments (DOE) and the ISO GUM supplement 1 uncertainty evaluation method to guide the instrument designer in the instrument configuration optimization. The proposed approach allows estimating, in the design phase, the overall instrumental uncertainty for different configurations, the instrument sensitivity to the accuracy in the measurements of its inputs and the effects on systematic and random measurement errors deriving from the choice of all instrumental variables. The use of data populations selected with the DOE criteria allows recovering valuable parameters equivalent to the sensitivity factors of the GUM linearized approach. The data analysis allows separating the critical factors that must be accurately controlled from those only weakly affecting the measurement uncertainty. The method has been applied to a case study where the metrological performances of a system devoted to the measurement of the acoustic radiation emitted by a vibrating panel in a reverberant enclosure had to be assessed.     

A Gaussian process and image registration based stitching method for high dynamic range measurement of precision surfaces

Optical instruments are widely used for precision surface measurement. However, the dynamic range of optical instruments, in terms of measurement area and resolution, is limited by the characteristics of the imaging and the detection systems. If a large area with a high resolution is required, multiple measurements need to be conducted and the resulting datasets needs to be stitched together. Traditional stitching methods use six degrees of freedom for the registration of the overlapped regions, which can result in high computational complexity. Moreover, measurement error increases with increasing measurement data. In this paper, a stitching method, based on a Gaussian process, image registration and edge intensity data fusion, is presented. Firstly, the stitched datasets are modelled by using a Gaussian process so as to determine the mean of each stitched tile. Secondly, the datasets are projected to a base plane. In this way, the three-dimensional datasets are transformed to two-dimensional (2D) images. The images are registered by using an (x, y) translation to simplify the complexity. By using a high precision linear stage that is integral to the measurement instrument, the rotational error becomes insignificant and the cumulative rotational error can be eliminated. The translational error can be compensated by the image registration process. The z direction registration is performed by a least-squares error algorithm and the (x, y, z) translational information is determined. Finally, the overlapped regions of the measurement datasets are fused together by the edge intensity data fusion method. As a result, a large measurement area with a high resolution is obtained. A simulated and an actual measurement with a coherence scanning interferometer have been conducted to verify the proposed method. The stitching result shows that the proposed method is technically feasible for large area surface measurement.     

A weighted least square based data fusion method for precision measurement of freeform surfaces

The trend towards product miniaturisation and multi-functionality constitutes a driving force for the application of complex surfaces in many fields such as advanced optics. The precision measurement of these surfaces should be carried out at multiple scales, of which process commonly involves several datasets obtained from different sensors. This paper presents a weighted least square based multi-sensor data fusion method for such measurement. The method starts from unifying the coordinate frames of the measured datasets using an intrinsic feature based surface registration method. B-spline surface is used to fit linear surface model to each identified overlapping area of the registered datasets, respectively. By forming a common basis function, the fitted surface models and the corresponding residuals are then combined to construct a weighted least square based data fusion system which is used to generate a fused surface model. An analysis of the uncertainty propagation in data fusion process is also given. Both computer simulation and actual measurement on various freeform surfaces are conducted to verify the validity of proposed method. The results indicate that the proposed method is capable of fusing multi-sensor measured datasets with notable reduction of the measurement uncertainty.     

多传感器信息融合在精密离心机动平衡测试系统中的应用

精密离心机动平衡测试系统由主轴上下两个测试面上的4个电容测微仪、地脚上的1个测力传感器及光栅测角元件组成。5个传感器分别测得离心机在准静态及以某一平衡转速运转时的一次谐波,测得1个地脚测力传感器输出的支反力及计算出4个电容测微仪输出的振动矢量。根据4个电容测微仪及1个地脚测力传感器的输出进行对比,给出5个传感器中某一传感器发生故障的判据。应用冗余技术与多传感器信息融合技术,使用计算机实现同步自动数据采集、处理及传感器故障报警。     

Structural damage detection using information fusion technique

In order to precisely identify the multiple damage locations of a structure, an information fusion technique is proposed in this paper. First, the frequency data and the mode shape data are regarded as two different information sources, and local decisions can be obtained using the multiple damage location assurance criterion (MDLAC) method and the frequency change damage detection method (FCDDM), respectively. Then, the local decisions are sent to a fusion centre. In the fusion centre, three main fusion approaches are applied to integrate all the local decisions. Finally, a global decision is acquired. The measurement errors are also taken into account in the fusion process. The numerical example and analysis demonstrate that the identification results of the three information fusion methods are better than those of the MDLAC method and the frequency change damage detection method.     

Study of weighted fusion methods for the measurement of surface geometry

Four types of weighted fusion methods, including pixel-level, least-squares, parametrical and non-parametrical, have been classified and theoretically analysed in this study. In particular, the uncertainty propagation of the weighted least-squares fusion was analysed and its relation to the Kalman filter was studied. In cooperation with different fitting models, these four weighted fusion methods can be applied to a range of measurement challenges. The experimental results of this study show that the four weighted fusion methods compose a computationally efficient and reliable system for multi-sensor measurement problems, especially for freeform surface measurement. A comparison of weighted fusion with residual approximation-based fusion has also been conducted by providing the input datasets with different noise levels and sample sizes. The results demonstrated that weighted fusion and residual approximation-based fusion are complementary approaches applicable to most fusion scenarios.     

Comparison of non-traditional technologies for material cutting from the point of view of surface roughness

The contribution deals with comparing non-traditional cutting technologies from the point of view of generated surface roughness. The comparison is carried out for technologies of abrasive waterjet cutting, oxygen cutting, laser and plasma cutting. As an experimental material, EN S355J0 structural steel and titanium CP-Ti Grade 2 was used. Surface topography measurement was done using a Mitutoyo Surftest SJ-401 instrument, and an optical profilometer MicroProf FRT was used for comparison and verification of acquired surface data. The aim of the contribution is to clarify, on the basis of experimental measurements, the behaviour of a topographic function after various ways of cutting; the knowledge of the function is necessary for solving both theoretical and practical technological tasks. The topographic function, which is a basis for the prediction of quality and control of the cutting processes, was derived in an original way. The use of individual technologies depends on technical and economical possibilities and on environmental influences. The results of measurement of cut surface roughness are in good accordance with the results of theoretical analysis.     

多传感器信息融合机器人工件识别

为了实现智能机器人对工件的自主识别,用遗传算法对模糊融合中多个参数进行优化,对多传感器信息在决策级进行模糊融合。仿真实验结果表明,该方法可以减少单一传感器的不确定性,增加结果的可分离性,从而提高系统对目标的识别能力。     

A Variational Approach to Multi-Modality Subsurface Data Inversion and Fusion Based on Shared Image Structure

In many subsurface sensing problems single-sensor information quality is poor, due to factors such as constrained sensing geometries and limited energy penetration. In such cases there is interest in combining information from multiple complementary sensing modalities. In this work, we describe a variational approach to joint multi-modality image formation which fuses boundary information that is shared between a group of heterogeneous imaging modalities. The specific application that motivates this work is the imaging of vulnerable atherosclerotic plaques. No single imaging modality has yet demonstrated the ability to detect these vulnerable lesions reliably. We demonstrate our approach by fusing shared boundary field estimates from MR and CT atherosclerotic lesion imagery into a single estimated underlying tissue boundary field, while simultaneously estimating and enhancing the original imagery. More generally, we present an approach for multi-modality subsurface data inversion and fusion based on shared image structure. This approach allows for better estimates of the characteristics and structure of the underlying scene.     

D-S证据理论在火灾探测中的应用

将多传感器信息融合技术引用到火灾探测领域，介绍了基于D-S证据理论的多传感器多个测量周期的信息融合（时空信息融合）的方法，并将该方法应用于多个火灾探测器的信息融合。模拟实验结果表明，与单个传感器相比，基于D-S证据理论的多传感器时空信息融合的结果具有较高的准确度和可信度。     

Combining multiple Large Volume Metrology systems: Competitive versus cooperative data fusion

Large Volume Metrology (LVM) tasks can require the concurrent use of several measuring systems. These systems generally consist of set of sensors measuring the distances and/or angles with respect to a point of interest so as to determine its 3D position. When combining different measuring systems, characterized by sensors of different nature, competitive or cooperative methods can be adopted for fusing data. Competitive methods, which are by far the most diffused in LVM, basically perform a weighted mean of the 3D positions determined by the individual measuring systems. On the other hand, for cooperative methods, distance and/or angular measurements by sensors of different systems are combined together in order to determine a unique 3D position of the point of interest.This paper proposes a novel cooperative approach which takes account of the measurement uncertainty in distance and angular measurements of sensors of different nature. The proposed approach is compared with classical competitive approaches from the viewpoint of the metrological performance. The main advantages of the cooperative approach, with respect to the competitive one, are: (i) it is the only option when the individual LVM systems are not able to provide autonomous position measurements (e.g., laser interferometers or single cameras), (ii) it is the only option when only some of the sensors of autonomous systems work correctly (for instance, a laser tracker in which only distance – not angular – measurements are performed), (iii) when using systems with redundant sensors (i.e. photogrammetric systems with a large number of distributed cameras), point localization tends to be better than that using the competitive fusion approach.     

SPOT WELDING QUALITY FUZZY CONTROL SYSTEM BASED ON MULTISENSOR INFORMATION FUSION

The multisensor information fusion technology is adopted for real time measuring the four parameters which are connected closely with the weld nugget size(welding current,electrode displacement,dynamic resistance,welding time),thus much more original information is obtained.In this way,the difficulty caused by measuring indirectly weld nugget size can be decreased in spot welding quality control,and the stability of spot welding quality can be improved.According to this method,two-dimensional fuzzy controllers are designed with the information fusion result as input and the thyristor control signal as output.The spot welding experimental results indicate that the spot welding quality intelligent control method based on multisensor information fusion technology can compensate the influence caused by variable factors in welding process and ensure the stability of welding quality.     

神经网络在多传感器信息集成与融合中的应用

多传感器信息集成与融合在处理信息中呈现出较好的实用性和优越性。介绍了有关多传感器信息集成与融合方面的基本知识，分析了传统的信息融合与运用神经网络实现多传感器信息融合的差异、实质，并给出了神经网络实现多传感器信息集成与融合的步骤。通过自动化程度很高的智能机器人对外界信息的集成与融合，用实验证明了神经网络比传统方法更能准确、可靠地描述传感器所获得的信息。     

Multi-agent decision fusion for motor fault diagnosis

Improvement of recognition rate is ultimate aim for fault diagnosis researchers using pattern recognition techniques. However, the unique recognition method can only recognise a limited classification capability which is insufficient for real-life application. An ongoing strategy is the decision fusion techniques. In order to avoid the shortage of single information source coupled with unique decision method, a new approach is required to obtain better results. This paper proposes a decision fusion system for fault diagnosis, which integrates data sources from different types of sensors and decisions of multiple classifiers. First, non-commensurate sensor data sets are combined using an improved sensor fusion method at a decision level by using relativity theory. The generated decision vectors are then selected based on correlation measure of classifiers in order to find an optimal sequence of classifiers fusion, which can lead to the best fusion performance. Finally, multi-agent classifiers fusion algorithm is employed as the core of the whole fault diagnosis system. The efficiency of the proposed system was demonstrated through fault diagnosis of induction motors. The experimental results show that this system can lead to super performance when compared with the best individual classifier with single-source data.     

激光 ICF 聚变靶丸轮廓测量系统标定方法

针对激光惯性约束聚变靶丸轮廓高精度测量系统的溯源标定问题,提出一种基于激光差动共焦测量原理的标定溯源方法。该方法基于激光差动共焦靶丸测量系统轴向响应曲线过零点精确对应测量系统焦点的性质,首先利用激光差动共焦靶丸测量系统测量经中国计量科学研究院计量检定的标准椭圆块的圆度,其次通过比对测量值和标准椭圆块圆度计量值,得出该系统测量传递系数为1.03,最后通过多次检定验证测量的方法,完成系统的高精度标定。实验结果显示,利用标定完成的系统进行激光聚变靶金属球比对测量,其标准差为37 nm,该标定方法的测量重复性为17 nm,其为靶丸表面轮廓的高精度测量奠定了坚实基础。     

先验概率与曲线概率阈值分割相结合的消噪方法

针对二维激光扫描仪获取的原始点云数据存在噪声点而影响测量精度的问题,提出了基于先验概率算法与曲线概率阈值分割的点云数据消噪方法。以异形截面环形件为实验对象,运用该消噪方法对原始数据进行处理,分别采用原始数据和消噪数据计算了环件台阶轴的高度,并将其与环件的真实值进行了对比。对比结果表明,消噪后数据的计算值更接近环件的真实值,从而验证该算法能有效提高测量精度。     

Study of spatial characteristics of near-coastal wind fields using UAV technology

Due to the problems of complex structure, high cost, and limited measurement range of traditional wind field measurement instruments, this paper used a combination of a modified anemometer and a multi-rotor UAV to make realistic measurements of the spatial wind field in near-shore waters. The feasibility of the equipment in the measurement is also demonstrated. To solve the problem that conventional instruments cannot acquire wind field data from multiple measurement points simultaneously, the paper introduces the dimensionless method and demonstrates the feasibility of the method through multiple perspectives. The measured data were noise-reduced and dimensionless processed to obtain the relationships between the dimensionless parameters between measurement points. The comparison analysis was carried out from different heights and directions to investigate the variation of the parameters in each plane and at each measurement point, and it was concluded that the wind field characteristics in a limited range can be inferred from a point within the height in the near-shore area. This conclusion offers the possibility of using a few measurement points to estimate and predict the wind field characteristics in nearshore waters.     

铁路钢轨缺陷伤损巡检与监测技术综述

我国高速铁路的快速发展给铁路钢轨基础设施的状态维护和安全保障带来挑战,综述了钢轨不同类型缺陷伤损的巡检与监测技术。回顾总结了国内外钢轨无损检测与监测技术的特点和局限性,以及钢轨在线巡检与监测仪器的应用情况。分析了多模态多物理集成传感与可视化成像检测、宏观与微观组织状态观测、无损检测与评估和健康监测集成融合,以及基于大数据管理的铁路钢轨快速巡检和实时监测等新技术研究进展,讨论了未来铁路钢轨缺陷伤损巡检及监测技术的发展趋势。     

多传感器数据融合技术在室内环境品质监控系统中的应用

为解决室内环境监控系统在信息处理中的误差和不确定性问题,提出一种多传感器自适应加权数据融合算法及基于模糊集理论融合算法的室内环境品质综合指数判定原则。该算法和判定原则以室内环境监测数据为研究对象,对室内环境品质因素进行综合等级评估,并根据评估结果控制驱动设备动作。实验表明:这种数据融合算法和判定原则是正确的,以此构造的室内环境品质监控系统具有较高的精度。