Measurement最新文献

英文中文

Shape sensing technology based on fiber Bragg grating for flexible instrument 基于光纤布拉格光栅的柔性仪表形状传感技术

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2024-11-28 DOI: 10.1016/j.measurement.2024.116282

Feiwen Wang, Qi Jiang, Maosheng Ye, Yingxuan Zhang, Lihui Yu

The absence of shape information can result in inaccurate localization and control of slender, flexible surgical instruments in narrow and tortuous lumen environments. In order to deal with the problem of shape sensing, this paper presents a prototype optical fiber shape sensor based on a triangular configuration, with each fiber containing 10 Fiber Bragg Grating (FBG) sensors. Given that the optical fiber is glued to the substrate, a four-layer strain transfer model is developed to analyze the adhesive’s effect on adhesion performance and to correct the curvature sensitivity coefficient matrix. This paper uses ANSYS software to generate 8 curves of varying complexity and analyze how the number of curvature sampling points affects the performance of three shape reconstruction algorithms. Finally, perception experiments on eight sets of predefined 3D spatial templates are carried out to verify the theoretical model. The experimental results indicate that the average end position error, average Frechet distance error, and average RMSE of the three algorithms were less than 1.1%, 1.6%, and 0.6% of the total sensor length, respectively. Therefore, the algorithms proposed in this paper effectively sense the sensor’s shape.

形状信息的缺失会导致在狭窄和弯曲的腔内环境中对细长、灵活的手术器械的定位和控制不准确。为了解决形状传感问题，本文提出了一种基于三角形结构的光纤形状传感器原型，每根光纤包含10个光纤布拉格光栅（FBG）传感器。在光纤与基材粘接的情况下，建立了四层应变传递模型，分析了粘接对粘接性能的影响，并对曲率敏感系数矩阵进行了修正。本文利用ANSYS软件生成了8条不同复杂度的曲线，分析了曲率采样点个数对三种形状重构算法性能的影响。最后，对8组预定义的三维空间模板进行了感知实验，验证了理论模型。实验结果表明，三种算法的平均末端位置误差、平均Frechet距离误差和平均RMSE分别小于传感器总长度的1.1%、1.6%和0.6%。因此，本文提出的算法可以有效地感知传感器的形状。

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引用次数: 0

Characterization and visualization of gas–liquid two-phase flow based on wire-mesh sensor 基于线网传感器的气液两相流表征与可视化

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2024-11-28 DOI: 10.1016/j.measurement.2024.116265

Mengyu Li , Ruiqi Wang , Wei Li , Hanqing Chen , Rui Min , Zhongke Gao

Gas–liquid two-phase flow is prevalent in both industrial production and the natural environment, making the exploration of its flow behavior critically important. In this study, we design a wire-mesh sensor and measurement system to conduct dynamic experiments on vertical upward gas–liquid two-phase flow, obtaining multi-channel data under various conditions. We develop an integrated limited penetrable visibility graph network (ILPVGNet) to quantitatively assess the dynamic transition from slug flow to bubble flow using network metrics. The results demonstrate that this approach effectively integrates multi-channel measurements, revealing the evolution dynamics and internal coupling mechanisms of gas–liquid two-phase flow. To better observe fine flow structures and details, we perform visualization and super-resolution analysis on the multi-channel data, significantly enhancing image clarity and restoring detailed features, thereby improving our ability to intuitively analyze complex gas–liquid flow behaviors.

气液两相流在工业生产和自然环境中普遍存在，因此对其流动特性的研究至关重要。在本研究中，我们设计了一种金属丝网传感器和测量系统，对垂直向上的气液两相流进行动态实验，获得不同条件下的多通道数据。我们开发了一个集成的有限穿透可见性图网络（ILPVGNet），利用网络指标定量评估段塞流到泡流的动态转变。结果表明，该方法有效地集成了多通道测量，揭示了气液两相流的演化动力学和内部耦合机制。为了更好地观察精细流动结构和细节，我们对多通道数据进行了可视化和超分辨率分析，显著提高了图像清晰度，恢复了细节特征，从而提高了直观分析复杂气液流动行为的能力。

引用次数: 0

Experiment study of long pulse eddy current / eddy current pulse-compression thermography detection on large area with low-frequency and low-current 低频低电流大范围长脉冲涡流/涡流脉冲压缩热成像检测实验研究

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2024-11-27 DOI: 10.1016/j.measurement.2024.116329

Xiang-Tao Xiao , Jie Xia , Song Tang , Chunyan Kong , Liang Zhang

Eddy current pulsed thermography (ECPT) excited by high-frequency and high-current is at risk of causing irreparable damage to the detection object. Furthermore, using a high-current excitation pattern in flammable and explosive environments is strictly prohibited. To cope with that limitation, long pulse eddy current thermography (LPECT) and eddy current pulse-compression thermography (ECPuCT), which work in weakened excitation conditions, are two reasonable technologies. The LPECT experiment in low-frequency (50 Hz) excitation shows that subsurface defects are detected when the current is 8.3 A. Compared with LPECT, the experiment results of 13-bit barker-coded ECPuCT show the possibility of further reducing the excitation current and improving the defect detection signal–noise ratio (SNR). With a two-coil magnetic core inductor, defects whose aspect ratio is over 6 in a large area are effectively detected by ECPuCT. That confirms LPECT and ECPuCT are suited for high-risk environments, such as the petrochemical field.

高频大电流激励下的涡流脉冲热成像（ECPT）存在对检测对象造成不可修复损伤的危险。此外，严格禁止在易燃易爆环境中使用大电流励磁方式。为了克服这一局限性，在弱激励条件下工作的长脉冲涡流热像仪（lpct）和涡流脉冲压缩热像仪（ECPuCT）是两种合理的技术。低频（50 Hz）激励下的lpct实验表明，当电流为8.3 A时，可以检测到亚表面缺陷。实验结果表明，与lpct相比，13位巴克编码的ecpct可以进一步降低激励电流，提高缺陷检测信噪比。ECPuCT采用双线圈磁芯电感，可以有效检测大面积宽高比大于6的缺陷。这证实了pect和ECPuCT适用于高风险环境，如石化领域。

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引用次数: 0

Opto-mechanical-thermal integration design of the primary optical system for a tri-band aviation camera 三波段航空相机主光学系统的光机热一体化设计

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2024-11-27 DOI: 10.1016/j.measurement.2024.116319

Kailin Zhang , Yue Pan , Xiping Xu , Liang Xu , Wancheng Liu , Motong Hu , Yi Lu , Yajie Cao

This paper presents a tri-band aviation camera that integrates short-wave infrared (SWIR, 0.9–1.7 μm), mid-wave infrared (MWIR, 3.7–4.8 μm) and visible (VIS, 0.486–0.656 μm) bands into a primary optical system and three subsystems, to enhance optical remote sensing capabilities. The focus is on opto-mechanical-thermal integration to effectively manage thermal stress and minimise deformation. Finite Element Analysis is employed to extract Zernike coefficients for deformation analysis, facilitating a comprehensive assessment of the camera’s performance across a temperature range of −40 °C to 60 °C. An innovative flexible support system is integrated to maintain the optimal surface figure of the primary mirror, further reducing thermal effects. Extensive empirical testing, including resolution and wavefront error detection, has validated the system’s robustness under various thermal conditions.

本文提出了一种三波段航空相机，将短波红外（SWIR, 0.9 ~ 1.7 μm）、中波红外（MWIR, 3.7 ~ 4.8 μm）和可见光（VIS, 0.486 ~ 0.656 μm）波段集成到主光学系统和三个子系统中，以增强光学遥感能力。重点是光-机械-热集成，以有效地管理热应力和最小化变形。采用有限元分析提取泽尼克系数进行变形分析，便于在- 40°C至60°C的温度范围内对相机的性能进行全面评估。集成了创新的柔性支撑系统，以保持主镜的最佳表面形状，进一步减少热效应。广泛的经验测试，包括分辨率和波前误差检测，验证了系统在各种热条件下的鲁棒性。

引用次数: 0

Calibration of multi-robot coordinates for collaborative wire arc additive manufacturing using cross-source 3D point cloud models 基于跨源三维点云模型的协同电弧增材制造多机器人坐标标定

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2024-11-27 DOI: 10.1016/j.measurement.2024.116294

Yongzhe Li , Wenkai Fu , Lingyi Meng , Xiaoyu Wang , Xiaochao Liu , Guangjun Zhang , Yijun Zhou

To increase the deposition deficiency and extend the working range of a single actuator, multi-robot collaborative wire arc additive manufacturing (MRC-WAAM) is proliferating in industry. Implementing an MRC-WAAM system should ensure precise transformations between a part frame and multiple robot work frames (RWFs) to facilitate fabrication quality. This paper proposes a novel calibration method for calculating the spatial correlation of RWFs by registering readings of multiple robot-carried 3D sensors. After formulating the influence of both translational and rotational errors, the principles and implementation are presented. To deal with the heterogeneity of cross-source point cloud models, an improved iterative closest point algorithm is articulated. The results of validation show that the proposed method can achieve high accuracy in orientation consistency of RWFs with an error of 0.091 deg, which is superior to the current state-of-the-art. This method can alleviate the consequences caused by the inconformity of multi-robot frames.

为了改善沉积缺陷和扩大单个驱动器的工作范围，多机器人协同电弧增材制造（MRC-WAAM）在工业上得到了广泛应用。实施MRC-WAAM系统应确保零件框架和多个机器人工作框架（rwf）之间的精确转换，以提高制造质量。本文提出了一种利用机器人携带的多个三维传感器的读数来计算RWFs空间相关性的标定方法。在阐述了平移误差和旋转误差的影响后，给出了其原理和实现方法。针对跨源点云模型的异构性，提出了一种改进的迭代最近点算法。验证结果表明，所提方法能达到较高的RWFs定向一致性精度，误差为0.091°，优于现有方法。该方法可以减轻多机器人机架不一致所带来的影响。

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引用次数: 0

Optimizing the quality characteristics of glass composite vias for RF-MEMS using central composite design, metaheuristics, and bayesian regularization-based machine learning 利用中心复合设计、元启发式和基于贝叶斯正则化的机器学习优化RF-MEMS玻璃复合通孔的质量特性

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2024-11-27 DOI: 10.1016/j.measurement.2024.116323

Dil Bahar, Akshay Dvivedi, Pradeep Kumar

Technological improvement in micro devices has accentuated the demand for glass and its composites. The μ-ECDM is emerging as an evolutionary technique for glass composite micro drilling, required for glass vias in the packaging of Radio Frequency Micro Electromechanical Systems (RF-MEMS). A response surface-based central composite design and metaheuristic algorithms have been employed to optimize the quality characteristics (in terms of deviation and smoothness) of drilled micro holes in glass epoxy composite. Subsequently, a Bayesian regularization-based Machine Learning (ML) algorithm has been deployed to substantiate the reliability of optimal outcomes from metaheuristic algorithms. At optimal point, process parameters were obtained around 48 V, 48 °C, 3.56 ms, 550 rpm with corresponding optimal outcomes of around 35 % smoothness and 20 % deviation of micro holes. The Bayesian regularization-based ML model validated the optimal outcomes with an insignificant deviation ranging from 0.45 to 2.87 %. Consequently, improvement in quality characteristics at optimal conditions has been conjectured for the industrial feasibility of the process in micro drilling the glass composite vias.

微型设备的技术改进增加了对玻璃及其复合材料的需求。μ-ECDM正在成为射频微机电系统（RF-MEMS）封装中的玻璃通孔所需的玻璃复合材料微钻的一种进化技术。采用响应面中心复合设计和元启发式算法优化玻璃环氧复合材料微孔的质量特性（偏差和平滑度）。随后，基于贝叶斯正则化的机器学习（ML）算法被部署，以证实从元启发式算法的最优结果的可靠性。在最佳点，工艺参数为48 V， 48°C, 3.56 ms, 550 rpm，相应的最佳结果为微孔光滑度约35%，偏差约20%。基于贝叶斯正则化的ML模型验证了最佳结果，偏差在0.45 - 2.87%之间。因此，在最佳条件下，质量特性的改善被推测为微钻玻璃复合材料过孔工艺的工业可行性。

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引用次数: 0

Modeling and calculation of magnetic noise in nanocrystalline magnetic shielding barrel considering anisotropy and stress effects 考虑各向异性和应力效应的纳米晶磁屏蔽桶磁噪声建模与计算

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2024-11-26 DOI: 10.1016/j.measurement.2024.116295

Xueping Xu , Zhenkai Zhao , Wei Liu , Weiwei Wu

Nanocrystalline alloys provide a low magnetic noise environment for spin-exchange relaxation-free (SERF) magnetic field measurement devices. However, the anisotropy of nanocrystalline significantly impacts their permeability and conductivity. Additionally, nanocrystalline strips on magnetic shielding barrel (MSB) increase magnetic noise due to bending stress. Therefore, this paper proposes an analytical model for magnetic noise calculation considering the anisotropy and bending stress of the nanocrystalline MSB. First, the magnetic noise computation model of the nanocrystalline MSB considering anisotropy, is established. Then, the bending stresses of the nanocrystalline ribbons with different curvatures are quantitatively modeled. The nanocrystalline loss under different bending stresses is separated by constructing a stress-dependent dynamic Jiles-Atherton (SDDJA) model. The magnetic noise of nanocrystalline MSB is calculated, and results show that the bending stress in the nanocrystalline MSB is positively correlated with hysteresis loss noise. The difference in average magnetic noises (1–100 Hz) of the nanocrystalline MSB between the experiment and calculation is only 4.17 %, which verifies the proposed method. This research can improve the accuracy of magnetic noise analysis and be the design benchmark for magnetic noise suppression.

纳米晶合金为无自旋交换弛豫（SERF）磁场测量装置提供了低磁噪声环境。然而，纳米晶的各向异性会严重影响其磁导率和导电性。此外，磁屏蔽筒（MSB）上的纳米晶条带会因弯曲应力而增加磁噪声。因此，本文提出了一种考虑到纳米晶 MSB 的各向异性和弯曲应力的磁噪声计算分析模型。首先，建立了考虑各向异性的纳米晶 MSB 磁噪声计算模型。然后，对不同曲率的纳米晶带的弯曲应力进行了定量建模。通过构建与应力相关的动态贾尔斯-阿瑟顿（SDDJA）模型，分离了不同弯曲应力下的纳米晶损耗。计算了纳米晶 MSB 的磁噪声，结果表明纳米晶 MSB 的弯曲应力与磁滞损耗噪声呈正相关。实验和计算得出的纳米晶 MSB 平均磁噪声（1-100 Hz）仅相差 4.17%，验证了所提出的方法。这项研究可以提高磁噪声分析的准确性，并成为磁噪声抑制的设计基准。

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引用次数: 0

A novel guided wave testing method for identifying rail web cracks using optical fiber Bragg grating sensing and orthogonal matching pursuit 提出了一种基于光纤光栅传感和正交匹配追踪的导波检测钢轨腹板裂纹的方法

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2024-11-26 DOI: 10.1016/j.measurement.2024.116317

Da-Zhi Dang, You-Wu Wang, Yi-Qing Ni

Ensuring safe operation of railway systems is essential. Detecting early-stage rail defects is crucial to prevent catastrophic incidents, such as derailments. Traditional manual inspection routines are often inefficient and lack robustness. This paper proposes a novel guided wave testing (GWT) method using optical fiber sensing combined with an orthogonal matching pursuit (OMP)-based data processing technique to detect rail damage by reconstructing defect-related reflective wave from complex raw signals. First, a finite element model (FEM) is constructed to simulate ultrasonic guided wave (UGW) signals and subsequent signal reconstruction using OMP demonstrates capabilities. In the experimental part, a rail segment with a vertical crack is installed with a fiber Bragg grating (FBG) sensor to receive UGW. The reconstructed signals confirm the effectiveness of our method in identifying the rail web crack location. This method offers a novel approach for rail crack identification, promoting more efficient inspection methods for extensive rail transportation networks.

确保铁路系统的安全运行至关重要。检测早期轨道缺陷对于防止灾难性事故（如脱轨）至关重要。传统的人工检测程序通常效率低下且缺乏鲁棒性。本文提出了一种基于光纤传感与正交匹配追踪（OMP）数据处理技术相结合的新型导波检测方法，通过从复杂原始信号中重构缺陷相关反射波来检测钢轨损伤。首先，建立了超声导波信号的有限元模拟模型，并利用OMP对信号进行了重构。在实验部分，在具有垂直裂纹的钢轨段上安装光纤布拉格光栅（FBG）传感器来接收UGW。重构信号验证了该方法对钢轨腹板裂缝位置识别的有效性。该方法为轨道裂缝识别提供了一种新的方法，为广泛的轨道交通网络提供了更有效的检测方法。

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引用次数: 0

Multi-vision-based displacement monitoring using global-local deep deblurring and Rauch-Tung-Striebel smoother 基于全局-局部深度去模糊和Rauch-Tung-Striebel平滑的多视觉位移监测

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2024-11-26 DOI: 10.1016/j.measurement.2024.116292

Peng “Patrick” Sun , Mohammad Vasef , Lin Chen

Measuring structural vibrations help assess dynamic performances of civil structures and infrastructure. Although conventional displacement sensors have been widely adopted, they are contact-based methods which lack scalability. Recently, computer vision (CV) has been applied as a noncontact method to measure displacements. However, fast speed of structural vibration (e.g., in shake table tests) can inevitably cause motion blur that imposes challenges in all image-based object/feature detections, especially for normal portable cameras (without high-speed shutters). To address such issue, the study proposed a multi-vision, full-field sensing framework with affordable cameras using a novel global–local detection and deblurring (GLDD) module, which was designed with a generative adversarial network (GAN)-based deblurring model to enhance detection efficiency and accuracy by restoring blemished videos from multiple perspectives. Rauch-Tung-Striebel (RTS) smoother was studied for data fitting using incomplete observations caused due to severe motion-induced blurs. A shake table test was conducted on an aluminum frame with cameras and conventional sensors monitoring the structural vibrations. Fiducial markers were used to track the movement of the key locations on the structure. Results showed that the proposed method is satisfactory to monitor shake table tests when compared to conventional measurements with root-mean-square errors of 0.51–0.95 mm. The proposed deblurring module restored misdetection by 92.1 %, 50.6 %, and 25.2 % for mild-, medium-, and severe-level motion blurs, respectively. Smoother-based data fitting outperformed filter-based one when dealing with highly blemished images. The proposed monitoring system with GLDD and RTS smoother-based data fitting provides a robust measurement solution when dealing with motion blurs.

测量结构振动有助于评估土木结构和基础设施的动力性能。传统的位移传感器虽然被广泛采用，但它们是基于接触的方法，缺乏可扩展性。近年来，计算机视觉（CV）作为一种非接触式的位移测量方法得到了广泛的应用。然而，快速的结构振动（例如，在振动台测试中）不可避免地会导致运动模糊，这对所有基于图像的对象/特征检测都构成了挑战，特别是对于普通便携式相机（没有高速快门）。为了解决这一问题，该研究提出了一种多视觉、全视野的传感框架，该框架使用一种新型的全局局部检测和去模糊（GLDD）模块，该模块设计了一个基于生成对抗网络（GAN）的去模糊模型，通过从多个角度恢复有缺陷的视频来提高检测效率和准确性。Rauch-Tung-Striebel （RTS）平滑研究了由于严重的运动引起的模糊而导致的不完整观测数据拟合。在一个铝制框架上进行了振动台试验，并安装了摄像头和传统传感器来监测结构振动。基准标记用于跟踪结构上关键位置的运动。结果表明，与常规测量方法相比，该方法能较好地监测振动台试验，均方根误差为0.51 ~ 0.95 mm。所提出的去模糊模块对轻度、中度和重度运动模糊的误检率分别为92.1%、50.6%和25.2%。在处理高度瑕疵的图像时，基于平滑的数据拟合优于基于滤波器的数据拟合。基于GLDD和RTS平滑数据拟合的监测系统在处理运动模糊时提供了一个强大的测量解决方案。

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引用次数: 0

Comparison of non-destructive testing methods of bolted joint status in steel structures 钢结构螺栓连接状态无损检测方法比较

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2024-11-26 DOI: 10.1016/j.measurement.2024.116318

Yang Zhao, Yanfang Zhang, Jiang Wang, Qingrui Yue, Hongbing Chen

This article analyzes the commonly used non-destructive testing methods of bolted joints. The contact method has high recognition accuracy for preload detection but is greatly affected by the coupling between sensors and interfaces. The non-contact method is convenient for collecting signals, but its recognition accuracy is greatly affected by environmental noise. For stress identification of steel plates, the identification accuracy is limited by the interface coupling situation and the shallow stress can only be identified. The evaluation of bolted joint status is mainly characterized by interface stiffness, parametric research should be conducted to explain the mapping mechanism between stress waves and interface stiffness. Machine learning can help improve the accuracy and efficiency of damage detection, but the end-to-end recognition model does not have physical significance. Therefore, the hardware and lightweight, intelligent recognition algorithms should be developed to improve the computational efficiency of the model and physical interpretability.

分析了螺栓连接常用的无损检测方法。接触式预紧力检测具有较高的识别精度，但受传感器与接口之间耦合的影响较大。非接触式方法采集信号方便，但其识别精度受环境噪声影响较大。对于钢板的应力识别，识别精度受界面耦合情况的限制，只能识别浅层应力。螺栓连接状态的评价主要以界面刚度为特征，应通过参数化研究来解释应力波与界面刚度之间的映射机制。机器学习可以帮助提高损伤检测的准确性和效率，但端到端识别模型不具有物理意义。因此，需要开发硬件和轻量级智能识别算法，以提高模型的计算效率和物理可解释性。

引用次数: 0

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