Measurement最新文献

英文中文

Spectral and global emissivity assessment by means of a novel infrared methodology 利用新型红外方法评估光谱和全球发射率

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2024-11-15 DOI: 10.1016/j.measurement.2024.116155

F. Di Carolo , L. Savino , C. Purpura , S. Cantoni , M. De Stefano Fumo , A. Del Vecchio , U. Galietti , Y. Guan , L. Lucchese , D. Palumbo , M. De Cesare

One of the most critical phases for a space mission involving a transportation system from space to Earth is the atmospheric re-entry since the high kinetic energy of orbital flight shall be reduced and converted in thermal energy. In these conditions, one of the design parameters that allow the TPS to withstand the aerothermal loads and to guarantee temperatures compatible with the selected materials is the emissivity. This parameter depends on several factors such as mechanical features, chemical composition, surface roughness, angle of sight, wavelength and temperature. For TPS materials, emissivity characteristics are difficult to evaluate due to harsh, and critical to simulate on-ground, operative environment characterized by material surface and plasma flow interaction.

In this work, a novel approach to detect the material spectral emissivity at several wavelengths, ranging from NIR (Near InfraRed) to the LW (Long Wavelength) spectral range, is presented. An experimental set-up composed of a black body, a pyrometer and two thermal cameras has been used for performing an accurate detection of the emissivity value at several wavelengths. The experimental analyses guarantee a systematic approach able to provide quantitative information of the spectral and global materials emissivity.

The ISiComp® material, a long carbon fiber reinforced SiC matrix composite (C/SiC) made in Italy and developed by CIRA and Petroceramics, has been used.

对于涉及从太空到地球的运输系统的太空任务来说，最关键的阶段之一是重返大气层，因为轨道飞行的高动能必须减少并转化为热能。在这些条件下，TPS 的设计参数之一是发射率，它能使 TPS 承受空气热负荷，并保证温度与所选材料相匹配。该参数取决于多个因素，如机械特征、化学成分、表面粗糙度、视角、波长和温度。对于 TPS 材料来说，由于材料表面与等离子体流相互作用的特点，发射率特性很难评估，而且对于模拟地面操作环境至关重要。实验装置由一个黑体、一个高温计和两个热像仪组成，用于精确检测多个波长的发射率值。ISiComp® 材料是一种长碳纤维增强碳化硅基复合材料（C/SiC），由意大利 CIRA 公司和 Petroceramics 公司共同开发。

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引用次数: 0

Wafer-level metal thin film thickness scanning based on multiple probe wavelengths nanosecond transient thermoreflectance 基于多探头波长的晶圆级金属薄膜厚度扫描纳秒瞬态热反射仪

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2024-11-15 DOI: 10.1016/j.measurement.2024.116247

Guoliang Ma, Biwei Meng, Shaojie Zhou, Yali Mao, Yunliang Ma, Xinglin Xiao, Chao Yuan

The present metal film thickness (d_Metal) measurement methods (e.g., profiler and electron microscope) are not able to simultaneously achieve non-invasion, wide measurement range, high-resolution, and wafer-level scanning. In this work, a d_Metal measurement method based on multiple probe wavelengths transient thermoreflectance (MW-TTR) is developed. Through a systematic sensitivity discussion, the guidance for reliable d_Metal measurement is illustrated theoretically. The realization of measuring different types of metals (Au, Al, Ni, Ti) is achieved with different wavelengths of probe lights. After the rigorous comparison with profiler and picosecond acoustic measurement, the accuracy of measuring nanosized film is verified (∼1% difference). The fitting uncertainties of d_Metal are < 5 % for Au and Al metals. The high-throughput wafer-level scanning measurement, with a spatial resolution of ∼ 50 μm, is also realized by integrating automatic displacement control and deep learning fast predicting model into MW-TTR. Spatial mapping of d_Metal is consistent with profiler measurement (∼5% deviation in 2000 μm length).

目前的金属膜厚度（dMetal）测量方法（如轮廓仪和电子显微镜）无法同时实现无损伤、宽测量范围、高分辨率和晶圆级扫描。在这项工作中，开发了一种基于多探针波长瞬态热反射（MW-TTR）的 dMetal 测量方法。通过系统的灵敏度讨论，从理论上说明了可靠的 dMetal 测量的指导原则。利用不同波长的探针光实现了对不同类型金属（金、铝、镍、钛）的测量。经过与轮廓仪和皮秒声学测量的严格比较，验证了测量纳米薄膜的准确性（差值∼1%）。对于金和铝金属，dMetal 的拟合不确定性为 5%。通过将自动位移控制和深度学习快速预测模型集成到 MW-TTR 中，还实现了空间分辨率为 ∼ 50 μm 的高通量晶圆级扫描测量。dMetal 的空间映射与轮廓仪测量结果一致（在 2000 μm 长度范围内偏差为 5%）。

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引用次数: 0

Array design and phase interferometer-based DOA estimation for diversely polarized antenna arrays 不同极化天线阵列的阵列设计和基于相位干涉仪的 DOA 估计

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2024-11-15 DOI: 10.1016/j.measurement.2024.116222

Mingchao Qu , Weijian Si , Ruizhi Liu

Rapidly acquiring the unambiguous Direction of Arrival (DOA) and polarization parameters of electromagnetic waves is a significant challenge in passive direction-finding systems. To this end, the array design method and unambiguous phase interferometer-based DOA estimation method for diversely polarized antenna arrays are proposed in this paper. First, the design of antenna polarization utilizes the polarization distance and the volume of a hyper-parallel polyhedron spanned by the receiving antenna polarization orientations. Then, the array element positions are designed according to the rule of the maximum admissible phase error and DOA estimation accuracy. Subsequently, we leverage the received signal power from different antennas to resolve ambiguities and improve robustness in low SNRs. Finally, Numerical simulations indicate that the proposed method has an unambiguous probability of more than 98 % at 0 dB in the frequency range from 0.8 GHz to 18 GHz. Practical experiments further validate the effectiveness of the proposed methods.

在无源测向系统中，快速获取明确的电磁波到达方向（DOA）和极化参数是一项重大挑战。为此，本文提出了多样化极化天线阵列的阵列设计方法和基于相位干涉仪的无差别 DOA 估计方法。首先，天线极化设计利用了极化距离和接收天线极化方向所跨的超平行多面体的体积。然后，根据最大允许相位误差和 DOA 估计精度规则设计阵元位置。随后，我们利用不同天线的接收信号功率来解决模糊问题，并提高低信噪比时的鲁棒性。最后，数值模拟表明，在 0 dB 的频率范围（0.8 GHz 至 18 GHz）内，建议的方法具有超过 98 % 的无歧义概率。实际实验进一步验证了所提方法的有效性。

引用次数: 0

A roadmap to fault diagnosis of industrial machines via machine learning: A brief review 通过机器学习诊断工业机器故障的路线图：简要回顾

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2024-11-15 DOI: 10.1016/j.measurement.2024.116216

Govind Vashishtha , Sumika Chauhan , Mert Sehri , Radoslaw Zimroz , Patrick Dumond , Rajesh Kumar , Munish Kumar Gupta

In fault diagnosis, machine learning theories are gaining popularity as they proved to be an efficient tool that not only reduces human effort but also identifies the health conditions of the machines automatically. In this work, an attempt has been made to systematically review the progress of machine learning theories in fault diagnosis from scratch to future perspectives. Initially, artificial intelligence came into the picture which started to weaken the human effort whose efficiency relies on feature extraction which depends on expert knowledge. The introduction of deep learning theories has reformed the fault diagnosis process by realising the artificial aid, encouraging end-to-end encryption in the diagnostic procedure. The deep learning theories have also filled the gap between the large amount of monitoring data and the health conditions of industrial machines. The future of deep learning theories i.e. transfer learning which uses the knowledge of one domain to another related domain during fault diagnosis has been reviewed. In last, the research trends of the machine learning theories have been briefly discussed along with their challenges in fault diagnostics.

在故障诊断领域，机器学习理论越来越受欢迎，因为它们被证明是一种高效的工具，不仅能减少人力，还能自动识别机器的健康状况。在这项工作中，我们试图系统地回顾机器学习理论在故障诊断领域从无到有的进展，并展望未来。最初，人工智能的出现开始削弱人力，而人力的效率依赖于专家知识的特征提取。深度学习理论的引入通过实现人工辅助、鼓励诊断过程中的端到端加密，改革了故障诊断过程。深度学习理论还填补了大量监测数据与工业机器健康状况之间的空白。深度学习理论的未来，即在故障诊断过程中将一个领域的知识应用到另一个相关领域的迁移学习，也得到了回顾。最后，简要讨论了机器学习理论的研究趋势及其在故障诊断中面临的挑战。

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引用次数: 0

Measurement and analysis of heterogeneous road transport parameters using Smart Traffic Analyzer and SUMO Simulator:An experimental approach 使用智能交通分析仪和 SUMO 模拟器测量和分析异质道路交通参数：一种实验方法

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2024-11-14 DOI: 10.1016/j.measurement.2024.116233

Santhiya Ravindran, Gurukarthik Babu Balachandran, Prince Winston David

The main objective of research work is to study the characteristics of heterogeneous road transport environment & compare its dynamic parameters with the performance metrics measurement in terms of error rate & accuracy for vehicle count and classification using Smart Traffic Analyzer (STA) and SUMO Traffic Simulator. SUMO GUI is a simple tool for microscopic traffic simulation and helps to obtain vehicle dynamic parameters in the easiest way. Experimental research is also carried out by capturing live traffic video at study area of four way intersection road and analyzed through STA. The outcome results from SUMO and STA explicit overall accuracy of 96.63 %, and 95.62 % for vehicle count with the error rate of 3.35% & 4.37%. Similarly for vehicle classification, it provides the overall accuracy of 97.21% and 83.01% with the error rate of 2.78% & 16.97% respectively.

研究工作的主要目的是研究异构道路交通环境的特征；比较其动态参数与使用智能交通分析仪（STA）和 SUMO 交通模拟器进行车辆计数和分类的误差率和准确率方面的性能指标测量。SUMO GUI 是一种用于微观交通模拟的简单工具，有助于以最简单的方式获取车辆动态参数。实验研究还通过捕捉四向交叉路口道路研究区域的实时交通视频，并通过 STA 进行分析。SUMO 和 STA 的结果表明，车辆计数的总体准确率分别为 96.63 % 和 95.62 %，误差率分别为 3.35% 和 4.37%。同样，车辆分类的总体准确率为 97.21% 和 83.01%，误差率分别为 2.78% 和 16.97%。

引用次数: 0

Weak ultrasonic guided wave signal recognition based on one-dimensional convolutional neural network denoising autoencoder and its application to small defect detection in pipelines 基于一维卷积神经网络去噪自编码器的弱超声导波信号识别及其在管道小缺陷检测中的应用

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2024-11-14 DOI: 10.1016/j.measurement.2024.116234

Jing Wu , Yingfeng Yang , Zeyu Lin , Yizhou Lin , Yan Wang , Weiwei Zhang , Hongwei Ma

Pipeline failures are often caused by the expansion of small defects. Structural damage to pipelines can lead to major safety accidents. When ultrasonic guided wave (UGW) technology is used for pipeline failure detection, the echoes produced by small defects manifest as weak UGW signals amidst significant noise. The low amplitude of these signals or complete drowning by noise makes them difficult to recognize. This study innovatively introduces a one-dimensional convolutional neural network denoising autoencoder (1DCNN-based DAE) for noise reduction in UGW signals using deep learning. To improve the conventional DAE, the model incorporated the Parametric Rectified Linear Unit (PReLU) activation function and a CNN for enhanced feature extraction, resulting in the proposed 1DCNN-based DAE. The model is trained on an extensive dataset of mixed signals with strong noise and their corresponding clean signals, enabling autonomous denoising in an unsupervised manner. Additionally, this paper proposes the application of the window-shifted power spectrum method for analyzing the denoised signals to identify and locate pipeline defects. The method involves traversing the signal with a window to intercept fragments, calculating their power, and plotting the power spectrum curve. Defects are then located based on the peak positions of this curve. Numerical simulation and experimental signals were used to validate the proposed method. Simulation results showed that the proposed 1DCNN-based DAE effectively improved the signal-to-noise ratio (SNR) of UGW mixed signals from −9 dB to 21.63 dB, representing an improvement of up to 30.63 dB. Experimental results demonstrated that the method accurately detected weak UGW signals from small defective pipes with a 2 % cross-section loss rate, achieving over 90 % recognition confidence and less than 1.5 % axial positioning error rate. In summary, the proposed 1DCNN-based DAE can effectively improve the SNR of the signal, reduce the noise in the UGW detection signal, and improve the sensitivity of defect identification; the window-shifted power spectrum method has a advantage in the accurate localization of defects.

管道故障通常是由微小缺陷的扩大造成的。管道结构性损坏可导致重大安全事故。当超声波导波 (UGW) 技术用于管道故障检测时，小缺陷产生的回波在巨大的噪声中表现为微弱的 UGW 信号。由于这些信号振幅较低或完全被噪声淹没，因此很难识别。本研究创新性地引入了一维卷积神经网络去噪自动编码器（基于 1DCNN 的 DAE），利用深度学习对 UGW 信号进行降噪。为了改进传统的 DAE，该模型纳入了参数整流线性单元（PReLU）激活函数和一个用于增强特征提取的 CNN，从而形成了所提出的基于 1DCNN 的 DAE。该模型在一个包含强噪声混合信号及其相应干净信号的广泛数据集上进行了训练，从而实现了无监督的自主去噪。此外，本文还提出应用窗移功率谱方法分析去噪信号，以识别和定位管道缺陷。该方法包括用窗口遍历信号以截取碎片，计算其功率并绘制功率谱曲线。然后根据该曲线的峰值位置对缺陷进行定位。数值模拟和实验信号被用来验证所提出的方法。仿真结果表明，所提出的基于 1DCNN 的 DAE 有效地改善了 UGW 混合信号的信噪比（SNR），从 -9 dB 提高到 21.63 dB，改善幅度高达 30.63 dB。实验结果表明，该方法能准确检测出截面损失率为 2% 的小型缺陷管道发出的微弱 UGW 信号，识别置信度超过 90%，轴向定位误差率低于 1.5%。综上所述，所提出的基于 1DCNN 的 DAE 能有效提高信号的信噪比，降低 UGW 检测信号中的噪声，提高缺陷识别的灵敏度；窗位移功率谱方法在缺陷精确定位方面具有优势。

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引用次数: 0

Proposal of an alternative method for residual stress measurements in clad nickel alloy after shot peening 关于喷丸强化后测量堆焊镍合金残余应力的替代方法的建议

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2024-11-14 DOI: 10.1016/j.measurement.2024.116230

Vladimir Ivanovitch Monine , João da Cruz Payão Filho , Mara Cardoso Gonçalves Rios Alonso Munhoz , Joaquim Teixeira de Assis

Cladding of Ni-based superalloy 625 is often used to enhance mechanical resistance and corrosion performance of the high strength low alloy pipeline, for use in CO₂ injection units in oil wells. It is well known that the use of the shot peening can improve its properties by creating a thin surface layer with compressive residual stresses. In this regard, the aim of this work is to propose an alternative method to evaluate the residual stresses of clad Ni-based superalloy 625 based on the deflection measurements of a thin strip separated from the shot peened samples by EDM cutting. The stress distribution in shot peened layer was measured by XRD sin²ψ method with electrolytic removal of surface layers. Measurements by the alternative and XRD method showed that the difference between the average stress values determined by these methods is 4%.

镍基超合金 625 的覆层通常用于提高高强度低合金管道的机械阻力和腐蚀性能，以用于油井中的二氧化碳注入装置。众所周知，使用喷丸强化可以通过产生具有压缩残余应力的薄表面层来改善其性能。因此，这项工作的目的是提出一种替代方法，通过电火花切割从喷丸强化样品中分离出来的薄带的挠度测量来评估包覆镍基超合金 625 的残余应力。在电解去除表面层的情况下，采用 XRD sin2ψ 方法测量了喷丸强化层中的应力分布。替代方法和 XRD 方法的测量结果表明，这两种方法测定的平均应力值相差 4%。

引用次数: 0

Retinal blood vessel segmentation using density-based fuzzy C-means clustering and vessel neighborhood connected component 利用基于密度的模糊 C-means 聚类和血管邻域连接成分进行视网膜血管分割

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2024-11-14 DOI: 10.1016/j.measurement.2024.116229

Kittipol Wisaeng

Retinal blood vessel segmentation is a crucial process in medical image analysis. However, it is often challenging due to the variation in color, shape, intensity, size, and contrast of blood vessels. Most of the relevant studies concentrate on algorithms based on supervised learning and few on deep learning. However, due to the several challenges in retinal image acquisition, these algorithms cannot deliver the highest possible level of accuracy. Therefore, this paper implements retinal blood vessel segmentation and classification (RBVSC) methods using density-based fuzzy C-means clustering and vessel neighborhood-connected components, hereafter denoted as DBFCM-VNCC. Initially, the given retinal images are preprocessed using the contrast enhancement method that involves Histogram Equalization with Variable Enhancement Degree (HEVED), selecting the appropriate color channel, optic disc elimination, and using a Gaussian filter, which removes the noise or artifacts from the retinal images. Then, a fully fuzzy C-means clustering is used for coarse segmentation of vessel lesions, which can detect the affected blood vessel features quite efficiently. Finally, structure-based algorithms based on vessel neighborhood-connected components based on mathematical dilation operators and local thicknesses are used to obtain accurate skeletonization and segmentation of the retinal vessels. The algorithm was assessed using three open-access retinal image databases: DRIVE, CHASE_DB1, and HRF, where it achieved mean sensitivity, specificity, accuracy, area overlap measure, and error rate scores of 98.16%, 98.74%, 97.68%, and 4.54%; 98.25%, 98.81%, 97.68%, 97.86%, and 2.14%; 98.22%, 98.78%, 97.56%, 97.40%, and 2.60% for segmenting the retinal vessel. This demonstrates the effectiveness of the proposed DBFCM-VNCC techniques.

视网膜血管分割是医学图像分析中的一个关键过程。然而，由于血管在颜色、形状、强度、大小和对比度方面存在差异，这通常是一项具有挑战性的工作。大多数相关研究都集中在基于监督学习的算法上，很少有关于深度学习的研究。然而，由于在视网膜图像采集方面存在诸多挑战，这些算法无法提供尽可能高的准确度。因此，本文利用基于密度的模糊 C-means 聚类和血管邻域连接组件实现了视网膜血管分割和分类（RBVSC）方法，以下简称 DBFCM-VNCC。首先，使用对比度增强方法对给定的视网膜图像进行预处理，该方法包括直方图均衡与可变增强度（HEVED）、选择适当的颜色通道、消除视盘以及使用高斯滤波器去除视网膜图像中的噪声或伪影。然后，使用全模糊 C-means 聚类对血管病变进行粗略分割，这可以相当有效地检测出受影响的血管特征。最后，利用基于数学扩张算子和局部厚度的血管邻域连接组件的结构化算法，对视网膜血管进行精确的骨架化和分割。该算法使用三个开放访问的视网膜图像数据库进行了评估：结果显示，该算法在分割视网膜血管方面的平均灵敏度、特异性、准确性、面积重叠度和错误率分别为 98.16%、98.74%、97.68% 和 4.54%；98.25%、98.81%、97.68%、97.86% 和 2.14%；98.22%、98.78%、97.56%、97.40% 和 2.60%。这证明了所提出的 DBFCM-VNCC 技术的有效性。

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引用次数: 0

Damage quantification using spectral response of a multi-degree-of-freedom system with spatial and temporal stiffness variations: Application to shear-type frames 利用具有空间和时间刚度变化的多自由度系统的频谱响应进行损伤量化：剪切型框架的应用

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2024-11-14 DOI: 10.1016/j.measurement.2024.116109

Sayandip Ganguly, Koushik Roy

The present study is aiming to develop a spectral response-based closed-form expression of maximum damage intensity in a multi-degree-of-freedom system considering temporal and spatial variations of stiffness. The time-dependent variation of stiffness is incorporated in the formulation to simulate opening–closing mechanism of crack. In addition, an expression for multiple damage severity at consecutive stories is also derived. For the validation of the present method, numerical model of a shear-type frame is thoroughly investigated. It is observed from the analysis that incorporation of spectral amplitudes at modulated frequencies in the formulation, improves the assessment of damage severity. Further, an experiment on a scale-down 6-story steel building is performed for practical insights followed by two case studies with real data of a full-scale tower and an instrumented building. As the assumed temporal variation of stiffness is not generic, it may mislead the accuracy of estimated damage quantity in other incompatible scenarios.

本研究旨在开发一种基于频谱响应的多自由度系统最大破坏强度闭式表达式，其中考虑了刚度的时间和空间变化。刚度的时空变化被纳入公式中，以模拟裂缝的开合机制。此外，还推导出了连续楼层多重损坏严重程度的表达式。为了验证本方法，对一个剪切型框架的数值模型进行了深入研究。从分析中可以看出，将调制频率的频谱振幅纳入公式中可以改善对破坏严重程度的评估。此外，还对一栋 6 层高的钢结构建筑进行了试验，以获得实用的见解，随后又对一栋全尺寸塔楼和一栋带仪器建筑的真实数据进行了案例研究。由于假定的刚度时间变化不是通用的，因此在其他不相容的情况下可能会误导估计破坏量的准确性。

引用次数: 0

Research on the construction of digital twin virtual model of coal mills 煤磨数字孪生虚拟模型构建研究

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2024-11-14 DOI: 10.1016/j.measurement.2024.116235

Weiming Yin , Yefa Hu , Guoping Ding , Lei Feng , Xuefei Chen

The high-precision model of coal mills can enable the accurate estimation of significant parameters that cannot be measured inside, as well as the necessary foundation for the intelligent coal-fired power unit. Addressing the problems of insufficient precision and lacking the online optimization capability of available coal mill models, this study proposes a method for constructing the digital twin virtual model of coal mills. Firstly, the working process and material flow of the equipment are analyzed, and a model of the coal mill considering the joint influence of the three forces and component abrasion is proposed. Then the model containing three processes of feeding-grinding, drying-separating, and component abrasion is constructed, and the parameters in the model are identified offline by the genetic algorithm. Finally, an online optimization mechanism considering workload adjustment and performance decay is designed, and the effectiveness of the developed method is verified by utilizing the actual operation data.

高精度的磨煤机模型可以准确估算磨煤机内部无法测量的重要参数，也是火电机组智能化的必要基础。针对现有磨煤机模型精度不够、缺乏在线优化能力等问题，本研究提出了一种磨煤机数字孪生虚拟模型的构建方法。首先，分析了设备的工作过程和物料流，提出了考虑三力共同影响和部件磨损的磨煤机模型。然后构建了包含给料-研磨、干燥-分离和部件磨损三个过程的模型，并通过遗传算法离线确定了模型中的参数。最后，设计了一种考虑工作量调整和性能衰减的在线优化机制，并利用实际运行数据验证了所开发方法的有效性。

引用次数: 0

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