Measurement最新文献_第3页

Intravascular catheter navigation and thrombus localization using hybrid electrodes electric field stereotaxis 利用混合电极电场立体定向法进行血管内导管导航和血栓定位

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2025-02-28 DOI: 10.1016/j.measurement.2025.117169

Yuxin Fang , Fan Yang , Wei He , Liang Tan , Zhenyou Liu , Wei Zhang , Xinheng Li , Pengbo Wang

Accurate navigation of intravascular interventional catheters and precise thrombus localization are critical for effective cerebrovascular procedures. Traditional digital subtraction angiography (DSA) offers navigation and visualization but is limited by two-dimensional imaging, increased procedural time, elevated costs, and radiation exposure. This study introduces the Hybrid Electrodes Electric Field Stereotaxis (HEEFS) method, which employs an internal localization electrode and external measurement electrodes to generate an electric field. The movement of the localization electrode induces changes in the field, enabling real-time 3D catheter navigation and thrombus localization based on the field’s uniqueness theorem. Unlike DSA, HEEFS achieves three-dimensional localization without additional imaging equipment and detects guidewire-thrombus contact via power monitoring. Simulation and porcine blood experiments demonstrated a localization accuracy of less than 1.7 mm and effective assessment of thrombus removal completeness. These results highlight the potential of HEEFS as a radiation-free, real-time navigation method, providing a novel solution for intravascular interventional surgeries.

血管内介入导管的精确导航和血栓的精确定位对于有效的脑血管手术至关重要。传统的数字减影血管造影术（DSA）可提供导航和可视化功能，但受到二维成像、手术时间延长、成本增加和辐射暴露等因素的限制。本研究介绍了混合电极电场立体定向法（HEEFS），该方法利用内部定位电极和外部测量电极产生电场。定位电极的移动会引起电场的变化，从而实现实时三维导管导航和基于电场唯一性定理的血栓定位。与 DSA 不同，HEEFS 无需额外的成像设备即可实现三维定位，并通过功率监测检测导丝与血栓的接触情况。模拟和猪血实验表明，定位精度小于 1.7 毫米，并能有效评估血栓清除的完整性。这些结果凸显了 HEEFS 作为一种无辐射实时导航方法的潜力，为血管内介入手术提供了一种新的解决方案。

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引用次数: 0

Research on calibration feature optimization and adaptive visual parameter adjustment for complex grating measurement

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2025-02-28 DOI: 10.1016/j.measurement.2025.117022

Hongyu Lv, Maoyue Li, Yuanqiang Su, Chenglong Zhang, Jingzhi Xu

This paper presents a novel method for the intelligent adjustment of vision parameters in structured light camera calibration under complex light conditions, aiming to enhance accuracy and reduce interference from human and external factors. Firstly, a self-learning weight calibration feature extraction model (SLWFE model) is developed to solve the coupled interference problem of calibration feature extraction. Secondly, we analyze the influence of focal length on structured light phase-height mapping accuracy and construct a grating calibration characteristic gradient filter function. The focus confidence evaluation model of calibration image is proposed, to realize the accurate calculation of optimal exposure time and lens ideal focus position, leading to the development of the grating calibration image characteristics optimization algorithm (GCICO). Finally, an intelligent parameterization device and control system were created, integrating the algorithm for experimental verification, achieving an average reprojection error of 0.018 pixels and an improvement of 70.49% over traditional methods.

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引用次数: 0

Robust masked time-frequency representation enabled decomposition algorithm of multi-component signals under unknown noise

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2025-02-28 DOI: 10.1016/j.measurement.2025.117141

Chao Ye, Tao Tao

With the continuous development of signal analysis technology, noise modeling has become almost as important as the study of useful signal structures. Breaking away from the simple assumption of modeling noise as Gaussian distribution, numerous scholars have effectively represented noise by the characteristic matching distribution, ultimately achieving more accurate and robust signal estimation and filtering computation. However, research on noise modeling for multi-component signal decomposition tasks is currently scarce. To address this issue, this paper proposes a robust masked time–frequency representation enabled chirp signal mode decomposition (RMCMD) algorithm. This algorithm focuses on both the structure of useful signals and noise modeling in signal decomposition tasks. Firstly, we use a proposed masked time–frequency representation method to decouple the multi-component decomposition computation task into single-component decomposition computation tasks in advance. Based on Bayesian statistical methods, we model the low-bandwidth prior of the demodulated signal of the component signal (CM) as an exponential distribution and the residual between the pre-processed signal and the extracted component signal as a mixture of Gaussian (MoG) distribution, Ultimately, we achieve a generative probability model for multi chirp component signal decomposition problems and construct an iterative multi-component signal decomposition calculation flow based on the expectation-maximum (EM) algorithm. Finally, through simulated and experimental signal calculation examples, the superiority of the proposed algorithm in terms of computational accuracy and robustness is effectively verified. The deep integration of noise modeling with signal decomposition computational tasks can be effectively enhanced by extending the application of noise modeling methods in multi-component signal decomposition tasks.

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引用次数: 0

Probabilistic quantitative inversion of wheel out-of-roundness using probabilistic learning on manifolds and dynamic model

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2025-02-28 DOI: 10.1016/j.measurement.2025.117166

Zhe Li , Meng Ma

Wheel out-of-roundness (OOR) significantly affects variations in metro train-induced environmental vibrations. Probabilistic quantitative inversion of wheel OOR on in-service trains is essential for predicting environmental vibrations and implementing necessary vibration control measures. Previous studies have indicated that using trackside vibrations to invert wheel OOR is an effective strategy. To quantify wheel OOR on in-service trains, this study proposes a probabilistic inversion method using probabilistic learning on manifolds and dynamic model. A dynamic model that considers both vertical wheel OOR and track irregularities was developed in the vehicle–track–tunnel–soil layer system to efficiently calculate the system dynamic responses. The model was validated using the measurement data. On this basis, a probabilistic learning on manifolds-based inversion method was developed to effectively learn the probabilistic mappings between wheel OOR and vibration source intensity. Ultimately, the inversion method was applied to invert wheel OOR statistics on in-service trains using monitored vibration source intensity data. The study results indicated that the proposed inversion method exhibits high accuracy, with average wheel OOR and mapped source intensity errors of 3.3 dB and 0.8 dB, respectively, between the inversion and validation data. The proposed inversion method effectively inverted wheel OOR statistics from measured vibration source intensities with an average mapped source intensity errors of 1.5 dB.

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引用次数: 0

Intelligent fault diagnosis method for rotating machinery based on sample multirepresentation information fusion under limited labeled samples conditions 有限标记样本条件下基于样本多表征信息融合的旋转机械智能故障诊断方法

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2025-02-28 DOI: 10.1016/j.measurement.2025.117164

Xin Yang , Xiangang Cao , Jiangbin Zhao , Xinyuan Zhang , Yong Duan , Luyang Shi

Currently, the use of graph neural networks (GNN) and other graph representation learning (GRL) methods to explore the relationships between labeled and unlabeled samples in rotating machinery measurement data has become a trend. This method aims to improve the fault diagnosis performance of diagnostic models when only a limited number of labeled samples are available. However, existing GRL methods suffer from incomplete representation of sample semantic information, insufficient extraction of associations between labeled and unlabeled samples, and overly smooth representation of sample node features, leading to suboptimal diagnostic accuracy. To address these issues, this paper proposes a fault diagnosis method based on sample multirepresentation information fusion (FD-SMRIF) with limited labeled samples. This method constructs multirepresentation information of sample measurement data, including comprehensive self-semantic information of fault samples from both global and local perspectives as well as multiple associations between labeled and unlabeled measurement data. It introduces an optimized variational autoencoder architecture to fuse the multirepresentation information of the samples, establishing an implicit distribution of the fusion features to increase their robustness. Additionally, a multiconditional latent space constraint module is constructed to adjust the latent space of the sample fault fusion feature representation, enhancing the effectiveness of the fusion features. The validity of this method for diagnosis with limited labeled samples was verified on two datasets. The results show that when the model was trained using only 5% labeled data, the accuracy rates of this method were 93.37% and 98.65%.

目前，使用图神经网络（GNN）和其他图表示学习（GRL）方法来探索旋转机械测量数据中已标记样本和未标记样本之间的关系已成为一种趋势。这种方法旨在提高诊断模型的故障诊断性能，因为只有有限数量的标记样本可用。然而，现有的 GRL 方法存在样本语义信息表示不完整、标记样本与非标记样本之间的关联提取不充分、样本节点特征表示过于平滑等问题，导致诊断精度不理想。为解决这些问题，本文提出了一种基于样本多表征信息融合（FD-SMRIF）的故障诊断方法，且标注样本有限。该方法构建了样本测量数据的多表征信息，包括故障样本从全局和局部角度的综合自语义信息，以及已标记和未标记测量数据之间的多重关联。它引入了一种优化的变异自动编码器架构来融合样本的多表征信息，并建立了融合特征的隐式分布，以提高其鲁棒性。此外，还构建了一个多条件潜空间约束模块来调整样本故障融合特征表示的潜空间，从而提高融合特征的有效性。在两个数据集上验证了该方法在有限标注样本下诊断的有效性。结果表明，当模型仅使用 5%的标注数据进行训练时，该方法的准确率分别为 93.37% 和 98.65%。

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引用次数: 0

Research on prediction model of converter steelmaking process based on multi-scale feature extraction and self-attention mechanism for multi-source domain asynchronous data

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2025-02-28 DOI: 10.1016/j.measurement.2025.117060

Yan Hu, Jia Tang, Baoshan Huang, Runying Xu

Using multi-source asynchronous data to predict the converter steelmaking process is crucial in metallurgical deep learning. Aiming at the problems of data lag, feature extraction, and complex conditions, this paper proposes a new method using multi-scale feature extraction and self-attention. The model utilizes a feature pyramid Network (FPN) to enhance feature learning and integrate different time Windows and slide size parameters (

δ

t) for best performance. The ReLU activation calculates the probability values of various conditions to output the condition with the highest probability. Experiments verify the effectiveness of the model. Within the time window of 15 seconds, the precision rate is 90.1%, the recall rate is 90.02%, and the F1 score is 89.7%. Experiments show that missing rope tension data has the greatest impact on the prediction, followed by smoke concentration and oxygen flow rate.

引用次数: 0

Data-mechanism fusion modeling and compensation for the spindle thermal error of machining center based on digital twin

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2025-02-28 DOI: 10.1016/j.measurement.2025.117152

Yingqiang Zheng, Hanbo Yang, Gegong Jiang, Shi Hu, Tao Tao, Xuesong Mei

Current methods for measuring thermal errors due to spindle operation often capture data from other components, complicating the measurement process. Furthermore, data-driven modeling struggles to integrate structural thermal deformation mechanisms, resulting in poor model generalization. To address these challenges, the data-mechanism fusion digital twin (DT) system for spindle thermal errors modeling and compensation is established, which encompasses the physical entity layer (PEL), DT prediction layer (DT-PL), and DT interaction service layer (DT-ISL). In the PEL, information from the machine tool is collected. In the DT-PL, the thermal error experiment is designed to identify the spindle thermal errors, and the multi-channel ensemble algorithm leveraging the physical mechanism (MCEA-PM) is proposed to calculate the spindle thermal deformation. The DT-ISL handles thermal error calculation, data visualization, and interaction with machine tools. The effectiveness of the proposed system was evaluated, achieving over 90 % prediction accuracy and a 72 % increase in machining accuracy during processing.

目前测量主轴运行引起的热误差的方法通常会采集其他部件的数据，从而使测量过程复杂化。此外，数据驱动建模难以整合结构热变形机制，导致模型通用性差。为了应对这些挑战，我们建立了用于主轴热误差建模和补偿的数据-机制融合数字孪生（DT）系统，该系统包括物理实体层（PEL）、DT 预测层（DT-PL）和 DT 交互服务层（DT-ISL）。在物理实体层（PEL）中，收集来自机床的信息。在 DT-PL 中，设计了热误差实验来识别主轴热误差，并提出了利用物理机制的多通道集合算法（MCEA-PM）来计算主轴热变形。DT-ISL 处理热误差计算、数据可视化以及与机床的交互。对所提系统的有效性进行了评估，结果显示预测精度超过 90%，加工过程中的加工精度提高了 72%。

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引用次数: 0

Bladder monitoring with time-frequency-difference electrical impedance tomography technique 利用时频差电阻抗断层扫描技术进行膀胱监测

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2025-02-28 DOI: 10.1016/j.measurement.2025.117162

Aiyada Phisaiphan, Phanpasorn Laor-Iam, Chamaiporn Sukjamsri, Anurak Dowloy, Taweechai Ouypornkochagorn

Patients experiencing urinary bladder dysfunction have difficulties sensing the bladder fullness and this affects their quality of life. Continuous bladder volume measurement assists in determining when voiding is necessary. Electrical Impedance Tomography (EIT) is a noninvasive technique that can be used for monitoring the bladder volume by producing images of conductivity distribution, and the time-difference EIT (tdEIT) is usually used. Even though the global conductivity obtained from the images is reported the possibility to reflect the volume, the investigations were restricted to well-controlled environments or short-term measurements where long-term measurements with moderate body movement allowed are practically expected. The movement, however, could immensely affect the image reconstruction. In this study, a time–frequency difference EIT (tfdEIT) is proposed. The tfdEIT was mainly based on frequency-difference imaging and adapted to use in the time-difference manner. The investigation was conducted on five female participants, allowing moderate movement of the lower body, and the measurement lasted for ∼1 h. Fabric electrodes were also developed for this purpose. The tfdEIT could serve long-term and movement-allowed measurement. Artifacts in the images were significantly reduced with the tfdEIT. The global conductivity of tfdEIT could reflect changes in bladder volume with a higher average correlation coefficient (r = 0.84) compared to tdEIT, where r was only 0.66. Fabric electrodes could handle long-term measurement with easy and firm attachment as well.

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引用次数: 0

A new method for change-point identification and RUL prediction of rolling bearings using SIC and incremental Kalman filtering

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2025-02-27 DOI: 10.1016/j.measurement.2025.117150

Junxing Li , Jiahui Fan , Zhihua Wang , Ming Qiu , Xiaofei Liu

Remaining useful life (RUL) prediction for rolling bearings is a key aspect in equipment prognosis and health management. To predict the RUL of rolling bearings, a two-stage degradation model that simultaneously considers environmental noise was first constructed to characterize the evolution of the health indicator (HI). A change-point identification method based on the Schwarz Information Criterion (SIC) is proposed to achieve adaptive switching between the two-stage degradation processes. Then, to overcome the issue of the Kalman filter (KF)-based method ignoring fluctuations in adjacent states, an incremental Kalman filtering (IKF) algorithm is proposed for RUL prediction using online observed HI data. Meanwhile, the expectation maximization (EM) algorithm is used in the absence of prior information to estimate the initial parameters. Finally, the effectiveness of this approach is verified using 16,004 rolling bearing test data points. The results show that the proposed method enhances RUL prediction accuracy by at least 57.12% over traditional KF-based methods and by 31.53% compared to methods that ignore environmental noise.

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引用次数: 0

Measurement of gas and liquid flow rates in two-phase pipe flows with distributed acoustic sensing

IF 5.2 2区工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY

Measurement

Pub Date : 2025-02-27 DOI: 10.1016/j.measurement.2025.117137

Kun-peng Zhang , Hao-chu Ku , Xiang-ge He , Hai-long Lu , Min Zhang , Chao-bin Guo

Accurately measuring gas–liquid two-phase flow rates in pipelines is critical for optimizing production processes. Distributed Acoustic Sensing (DAS) has emerged as a promising technique for conducting long-term measurements of pipeline flow rates. The effectiveness of DAS in measuring two-phase flow rates is evaluated in this study. An experimental setup was designed to simulate a gas–liquid two-phase flow, employing DAS to detect acoustic signals along its length. Based on the results, a method for measuring single-phase and two-phase flow rates with DAS was developed. Key findings include: (1) As flow rates increase, the vibrational energy the flow induces also rises. Vibrational energy from single-phase gas flow is significantly weaker than that from liquid-containing flows. (2) An increase in liquid flow rate results in a negative shift in the detected characteristic operating frequency. (3) The spectrum reveals two dominant frequency bands, with low liquid and gas flow rates exhibiting narrower main bands, lower energy levels, and strong autocorrelation.

精确测量管道中的气液两相流量对于优化生产流程至关重要。分布式声学传感技术（DAS）已成为对管道流速进行长期测量的一项前景广阔的技术。本研究评估了 DAS 在测量两相流速方面的有效性。我们设计了一套实验装置来模拟气液两相流，并利用 DAS 探测管道沿线的声学信号。根据研究结果，开发了一种利用 DAS 测量单相和两相流速的方法。主要发现包括(1) 随着流速的增加，流动引起的振动能量也会增加。单相气体流动产生的振动能量明显弱于含液体流动产生的振动能量。(2) 液体流速的增加会导致检测到的特征工作频率发生负偏移。(3) 频谱显示出两个主要频带，低液体和气体流速显示出更窄的主频带、更低的能级和更强的自相关性。

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引用次数: 0