Pub Date : 2026-02-01DOI: 10.1016/j.measurement.2026.120675
Xiangpeng Zhang , Wenjie Tian , Hong Liu , Weiguo Gao
Parallel mechanisms are widely used due to their high rigidity, high precision and fast response characteristics. Absolute positioning accuracy is the foundation for ensuring the performance of the mechanism, while kinematic calibration is an effective way to improve the performance of the platform. Error modeling and parameter identification represent two critical stages in the kinematic calibration process. This paper presents a graphical interpretation of error modeling based on the Abbe criterion. Combined with the screw theory, the geometric error model of the 6-UPS Stewart platform was established. Subsequently, the probabilistic ellipsoid was used to evaluate the pose repeatability, and a weighted parameter identification algorithm based on direction decoupling was proposed. The core of this algorithm lies in establishing the intrinsic relationship between pose repeatability and pose error. Thirdly, the prediction accuracy of the weighted algorithm and the non-weighted algorithm was compared through computer simulation, and a method for determining the optimal weight using the particle swarm optimization algorithm was proposed. Finally, the accuracy and reliability of the weighting algorithm were experimentally verified on the robot prototype.
{"title":"A kinematic calibration method for parallel mechanisms integrating error modeling using Abbe criterion with pose repeatability weighting identification","authors":"Xiangpeng Zhang , Wenjie Tian , Hong Liu , Weiguo Gao","doi":"10.1016/j.measurement.2026.120675","DOIUrl":"10.1016/j.measurement.2026.120675","url":null,"abstract":"<div><div>Parallel mechanisms are widely used due to their high rigidity, high precision and fast response characteristics. Absolute positioning accuracy is the foundation for ensuring the performance of the mechanism, while kinematic calibration is an effective way to improve the performance of the platform. Error modeling and parameter identification represent two critical stages in the kinematic calibration process. This paper presents a graphical interpretation of error modeling based on the Abbe criterion. Combined with the screw theory, the geometric error model of the 6-U<u>P</u>S Stewart platform was established. Subsequently, the probabilistic ellipsoid was used to evaluate the pose repeatability, and a weighted parameter identification algorithm based on direction decoupling was proposed. The core of this algorithm lies in establishing the intrinsic relationship between pose repeatability and pose error. Thirdly, the prediction accuracy of the weighted algorithm and the non-weighted algorithm was compared through computer simulation, and a method for determining the optimal weight using the particle swarm optimization algorithm was proposed. Finally, the accuracy and reliability of the weighting algorithm were experimentally verified on the robot prototype.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"268 ","pages":"Article 120675"},"PeriodicalIF":5.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-02-01DOI: 10.1016/j.measurement.2026.120678
Xuegang Li , Yuanyue Pu , Shengbao Qin , Huayan Pu , Yudong Zhang , Xiaoxi Ding , Wenbin Huang
Gears play a vital role in industrial transmission systems, yet they are prone to degradation and localized faults such as spalling under complex working conditions. Early and accurate fault diagnosis is essential to ensure operational reliability and reduce maintenance costs. Dynamic transmission error (DTE) is defined as the deviation between the actual and theoretical meshing positions of gears. It serves as a key indicator of meshing accuracy and dynamic behavior, directly reflecting the health status of gears. However, conventional analytical approaches for DTE calculation rely on precise physical modeling and known parameters, limiting their adaptability in real-world applications. Meanwhile, encoder-based DTE measurement methods are often impractical for enclosed gearboxes due to installation constraints and susceptibility to environmental interference. To address these limitations, this paper proposes an in-situ DTE-driven lightweight intelligent fault diagnosis (IDLIFD) framework for gear spalling fault diagnosis. First, an in-situ DTE reconstruction and enhancement method is developed to obtain meshing deviation information from in-situ vibration signals, enabling practical DTE measurement in enclosed environments. Then, a lightweight wide-area deconstruction network (WDNet) is designed to extract discriminative spalling-related features from enhanced DTE signals while maintaining a compact structure and low computational complexity. Finally, experimental validation on a self-made gearbox test bench demonstrates that the proposed IDLIFD framework outperforms existing computational methods and lightweight diagnosis models in terms of DTE calculation, diagnosis accuracy, and real-world deployment.
{"title":"In-situ dynamic transmission error -driven lightweight wide-area deconstruction network for gear spalling fault intelligent diagnosis","authors":"Xuegang Li , Yuanyue Pu , Shengbao Qin , Huayan Pu , Yudong Zhang , Xiaoxi Ding , Wenbin Huang","doi":"10.1016/j.measurement.2026.120678","DOIUrl":"10.1016/j.measurement.2026.120678","url":null,"abstract":"<div><div>Gears play a vital role in industrial transmission systems, yet they are prone to degradation and localized faults such as spalling under complex working conditions. Early and accurate fault diagnosis is essential to ensure operational reliability and reduce maintenance costs. Dynamic transmission error (DTE) is defined as the deviation between the actual and theoretical meshing positions of gears. It serves as a key indicator of meshing accuracy and dynamic behavior, directly reflecting the health status of gears. However, conventional analytical approaches for DTE calculation rely on precise physical modeling and known parameters, limiting their adaptability in real-world applications. Meanwhile, encoder-based DTE measurement methods are often impractical for enclosed gearboxes due to installation constraints and susceptibility to environmental interference. To address these limitations, this paper proposes an in-situ DTE-driven lightweight intelligent fault diagnosis (IDLIFD) framework for gear spalling fault diagnosis. First, an in-situ DTE reconstruction and enhancement method is developed to obtain meshing deviation information from in-situ vibration signals, enabling practical DTE measurement in enclosed environments. Then, a lightweight wide-area deconstruction network (WDNet) is designed to extract discriminative spalling-related features from enhanced DTE signals while maintaining a compact structure and low computational complexity. Finally, experimental validation on a self-made gearbox test bench demonstrates that the proposed IDLIFD framework outperforms existing computational methods and lightweight diagnosis models in terms of DTE calculation, diagnosis accuracy, and real-world deployment.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"268 ","pages":"Article 120678"},"PeriodicalIF":5.6,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Surface finish is a decisive quality attribute in direct metal laser sintering, as it governs fatigue life, wear behavior, and the extent of post-processing required. In this study, a Conditional Value-at-Risk Desirability Multi-response Bayesian Optimization (CD-MBO) methodology is applied. This method is designed to penalize parameter settings that produce unstable outcomes and to enable robust multi criteria process decisions by explicitly capturing variability and tail-risk in the surface roughness responses. SS316L specimens were produced according to an L9 orthogonal array on an EOS M 290 system, with laser power, scan speed, and layer thickness considered as the primary process variables. Surface roughness metrics (Ra, Rq, Rz) were subsequently quantified using a Surftest SJ-210 profilometer. The CD-MBO approach aggregated tail-sensitive CVaR0.80 values of each metric into a weighted desirability function, with uncertainty modeled using a Bayesian bootstrap. The optimal parameter setting was identified as 330 W, 900 mm/s, and 80 µm, yielding Ra = 5.75 ± 0.15 µm, Rq = 6.67 ± 0.01 µm, and Rz = 28.84 ± 0.05 µm, with significantly reduced upper-tail behavior compared to alternative configurations. Sensitivity analyses confirmed that the top-ranked solution was invariant to weighting schemes (variance-based vs. principal component analysis-based) and risk-penalty levels (k = 0 - 0.5). SEM fractography further validated the suppression of porosity and lack-of-fusion defects at the optimal setting. This study makes an innovative contribution to the field through the development of a distribution-aware, metrologically informed optimization methodology for laser powder bed fusion surface finish, which leverages advanced statistical modelling techniques alongside risk-based decision metrics. The study thereby extends the field beyond deterministic quality optimization towards uncertainty-aware measurement decision-making, as it is appropriate to the scope and ambition of the field of Measurement Science.
{"title":"Risk-aware multi-response optimization of surface roughness in laser additive manufacturing via conditional value-at-risk desirability modelling","authors":"Geetha Narayanan Kannaiyan , Nagasuneetha Darla , Gangadhara Rao Ponugoti , Venkata Phani Babu Vemuri , Bridjesh Pappula , Seshibe Makgato","doi":"10.1016/j.measurement.2026.120655","DOIUrl":"10.1016/j.measurement.2026.120655","url":null,"abstract":"<div><div>Surface finish is a decisive quality attribute in direct metal laser sintering, as it governs fatigue life, wear behavior, and the extent of post-processing required. In this study, a Conditional Value-at-Risk Desirability Multi-response Bayesian Optimization (CD-MBO) methodology is applied. This method is designed to penalize parameter settings that produce unstable outcomes and to enable robust multi criteria process decisions by explicitly capturing variability and tail-risk in the surface roughness responses. SS316L specimens were produced according to an L9 orthogonal array on an EOS M 290 system, with laser power, scan speed, and layer thickness considered as the primary process variables. Surface roughness metrics (Ra, Rq, Rz) were subsequently quantified using a Surftest SJ-210 profilometer. The CD-MBO approach aggregated tail-sensitive CVaR<sub>0.80</sub> values of each metric into a weighted desirability function, with uncertainty modeled using a Bayesian bootstrap. The optimal parameter setting was identified as 330 W, 900 mm/s, and 80 µm, yielding Ra = 5.75 ± 0.15 µm, Rq = 6.67 ± 0.01 µm, and Rz = 28.84 ± 0.05 µm, with significantly reduced upper-tail behavior compared to alternative configurations. Sensitivity analyses confirmed that the top-ranked solution was invariant to weighting schemes (variance-based vs. principal component analysis-based) and risk-penalty levels (<em>k</em> = 0 - 0.5). SEM fractography further validated the suppression of porosity and lack-of-fusion defects at the optimal setting. This study makes an innovative contribution to the field through the development of a distribution-aware, metrologically informed optimization methodology for laser powder bed fusion surface finish, which leverages advanced statistical modelling techniques alongside risk-based decision metrics. The study thereby extends the field beyond deterministic quality optimization towards uncertainty-aware measurement decision-making, as it is appropriate to the scope and ambition of the field of Measurement Science.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"268 ","pages":"Article 120655"},"PeriodicalIF":5.6,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172029","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-31DOI: 10.1016/j.measurement.2026.120607
Daniel Hernandez , Taewoo Nam , Eunwoo Lee , Aiming Lu , Christopher P. Favazza , Eric G. Stinson , David A. Woodrum , Myung-Ho In , Kyoung-Nam Kim
Microwave ablation (MWA) guided by magnetic resonance imaging (MRI) is an effective approach for minimally invasive tumor treatment, combining MRI’s soft tissue image contrast and temperature mapping capabilities with the ablative power of microwave energy. However, MRI-guided MWA faces a significant challenge with image noise generated by electromagnetic (EM) interference, which degrades image quality and limits real-time monitoring accuracy. As an initial step to address this problem, we introduce a novel noise characterization method for MRI-guided MWA using EM modeling and simulation. Empirically acquired noise data from controlled phantom experiments was used to develop a theoretical framework for simulating interactions between MRI components and the MWA device. The simulations included key system components: the RF transmit coil, receiver coil, phantom, and MWA probe, and produced a signal–noise map that closely matched the experimental data, effectively replicating observed noise patterns. To demonstrate the use of these simulations for practical applications, we evaluated a simple filter circuit for its effectiveness in reducing noise and validated simulation results through benchwork, which showed significant improvements. The results suggest that this approach provides valuable insights into the underlying noise mechanisms and can inform potential strategies for noise mitigation, offering a practical tool for optimizing MRI-guided MWA and enhancing the efficacy of interventional MRI procedures.
{"title":"Electromagnetic noise characterization in MRI-guided microwave ablation","authors":"Daniel Hernandez , Taewoo Nam , Eunwoo Lee , Aiming Lu , Christopher P. Favazza , Eric G. Stinson , David A. Woodrum , Myung-Ho In , Kyoung-Nam Kim","doi":"10.1016/j.measurement.2026.120607","DOIUrl":"10.1016/j.measurement.2026.120607","url":null,"abstract":"<div><div>Microwave ablation (MWA) guided by magnetic resonance imaging (MRI) is an effective approach for minimally invasive tumor treatment, combining MRI’s soft tissue image contrast and temperature mapping capabilities with the ablative power of microwave energy. However, MRI-guided MWA faces a significant challenge with image noise generated by electromagnetic (EM) interference, which degrades image quality and limits real-time monitoring accuracy. As an initial step to address this problem, we introduce a novel noise characterization method for MRI-guided MWA using EM modeling and simulation. Empirically acquired noise data from controlled phantom experiments was used to develop a theoretical framework for simulating interactions between MRI components and the MWA device. The simulations included key system components: the RF transmit coil, receiver coil, phantom, and MWA probe, and produced a signal–noise map that closely matched the experimental data, effectively replicating observed noise patterns. To demonstrate the use of these simulations for practical applications, we evaluated a simple filter circuit for its effectiveness in reducing noise and validated simulation results through benchwork, which showed significant improvements. The results suggest that this approach provides valuable insights into the underlying noise mechanisms and can inform potential strategies for noise mitigation, offering a practical tool for optimizing MRI-guided MWA and enhancing the efficacy of interventional MRI procedures.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"268 ","pages":"Article 120607"},"PeriodicalIF":5.6,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146096171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-31DOI: 10.1016/j.measurement.2026.120643
Kang Bi , Xinyu Shi , Da Wan , Weijiu Cui , Haining Zhou , Chengpeng Sun , Peng Du , Hiroatsu Fukuda
Accurate end-effector positioning is crucial for mobile robotic platforms (MCP) in automated on-site construction tasks. This paper introduces a real-time visual positioning system based on the AprilTag algorithm, specifically developed to enhance the end-effector accuracy of MCPs used in construction. The solution builds upon an autonomous navigation MCP platform by incorporating Fiducial Marker System (FMS) and automatic interpolation compensation algorithm based on camera-specific error model. Experimental results demonstrate a significant improvement in accuracy: the enhanced algorithm achieved an average positioning accuracy of 1.08 mm—17.56% improvement over the native AprilTag method—and positioning stability improved by 85.7% (with a standard deviation of 0.38 mm). Comprehensive experiments, including random point positioning, curve fitting, and physical assembly tasks, confirmed the system’s robustness, repeatability, and industrial applicability. This method enables MCPs to autonomously adjust robot working path in real time according to dynamic on-site conditions, adapt to unpredictable construction environments, and significantly enhance construction precision.
准确的末端执行器定位是移动机器人平台（MCP）在自动化现场施工任务中的关键。本文介绍了一种基于AprilTag算法的实时视觉定位系统，该系统是专门为提高建筑用mcp末端执行器的精度而开发的。该解决方案建立在自主导航MCP平台上，结合了基准标记系统（FMS）和基于相机特定误差模型的自动插值补偿算法。实验结果表明，改进后的算法在精度上有了显著的提高，平均定位精度比原生AprilTag方法提高了1.08 mm - 17.56%，定位稳定性提高了85.7%（标准差为0.38 mm）。综合实验，包括随机点定位、曲线拟合和物理装配任务，证实了系统的鲁棒性、可重复性和工业适用性。该方法使mcp能够根据现场动态情况实时自主调整机器人工作路径，适应不可预测的施工环境，显著提高施工精度。
{"title":"Research on real-time monocular visual positioning system for mobile robotic construction with automated error compensation","authors":"Kang Bi , Xinyu Shi , Da Wan , Weijiu Cui , Haining Zhou , Chengpeng Sun , Peng Du , Hiroatsu Fukuda","doi":"10.1016/j.measurement.2026.120643","DOIUrl":"10.1016/j.measurement.2026.120643","url":null,"abstract":"<div><div>Accurate end-effector positioning is crucial for mobile robotic platforms (MCP) in automated on-site construction tasks. This paper introduces a real-time visual positioning system based on the AprilTag algorithm, specifically developed to enhance the end-effector accuracy of MCPs used in construction. The solution builds upon an autonomous navigation MCP platform by incorporating Fiducial Marker System (FMS) and automatic interpolation compensation algorithm based on camera-specific error model. Experimental results demonstrate a significant improvement in accuracy: the enhanced algorithm achieved an average positioning accuracy of 1.08 mm—17.56% improvement over the native AprilTag method—and positioning stability improved by 85.7% (with a standard deviation of 0.38 mm). Comprehensive experiments, including random point positioning, curve fitting, and physical assembly tasks, confirmed the system’s robustness, repeatability, and industrial applicability. This method enables MCPs to autonomously adjust robot working path in real time according to dynamic on-site conditions, adapt to unpredictable construction environments, and significantly enhance construction precision.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"268 ","pages":"Article 120643"},"PeriodicalIF":5.6,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-31DOI: 10.1016/j.measurement.2026.120672
Tingyu Zhang, Kai Liu, Guangbo Nie, Guoqiang Gao, Guangning Wu
Difficult data collection and limited labeled samples of partial discharge (PD) signals from vehicle cable terminals lead to insufficient recognition accuracy and poor generalization capability in pattern recognition networks. To address these issues, this study proposes a data augmentation method based on image generation. Initially, the Markov Transition Field (MTF) serves to translate the original one-dimensional PD signals into two-dimensional images, building up a foundational PD sample library. Furthermore, within the diffusion model framework, conditional information and supervised contrastive learning mechanisms are innovatively integrated to achieve high-quality conditional PD sample generation. Experiments demonstrate that the PD images generated by this method are of significantly higher quality than those generated by other comparative models. Augmenting the original sample library with the generated samples effectively improves the recognition performance of various classification models. Finally, an accuracy of up to 98.54% in PD pattern classification is achieved using an improved residual network model, significantly enhancing the PD diagnosis capability for vehicle cable terminals.
{"title":"Application of an improved diffusion model-based data augmentation method in partial discharge pattern recognition for vehicle cable terminals","authors":"Tingyu Zhang, Kai Liu, Guangbo Nie, Guoqiang Gao, Guangning Wu","doi":"10.1016/j.measurement.2026.120672","DOIUrl":"10.1016/j.measurement.2026.120672","url":null,"abstract":"<div><div>Difficult data collection and limited labeled samples of partial discharge (PD) signals from vehicle cable terminals lead to insufficient recognition accuracy and poor generalization capability in pattern recognition networks. To address these issues, this study proposes a data augmentation method based on image generation. Initially, the Markov Transition Field (MTF) serves to translate the original one-dimensional PD signals into two-dimensional images, building up a foundational PD sample library. Furthermore, within the diffusion model framework, conditional information and supervised contrastive learning mechanisms are innovatively integrated to achieve high-quality conditional PD sample generation. Experiments demonstrate that the PD images generated by this method are of significantly higher quality than those generated by other comparative models. Augmenting the original sample library with the generated samples effectively improves the recognition performance of various classification models. Finally, an accuracy of up to 98.54% in PD pattern classification is achieved using an improved residual network model, significantly enhancing the PD diagnosis capability for vehicle cable terminals.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"268 ","pages":"Article 120672"},"PeriodicalIF":5.6,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-31DOI: 10.1016/j.measurement.2026.120659
Yuanju Wang, Runzhou You, Liang Ren, Xin Feng, Jiang Cui
Pipeline deformation monitoring plays a crucial role in ensuring the safety of oil and gas transportation. The inverse finite element method (iFEM) is an emerging deformation reconstruction approach that can be implemented without invoking force equilibrium. While limited experimental work is available for three-dimensional deformation reconstruction of pipelines. To overcome this limitation, this paper proposes an integrated approach combining the inverse finite element method (iFEM) with FBG/DOFS-based strain sensing technology for three-dimensional deformation reconstruction of pipelines. Two experimental studies were conducted, encompassing the three-dimensional deformation evaluative testing and the large-scale model testing. During the three-dimensional deformation evaluative testing, a numerical model was established, and the effect of strain sensor location and discrete element level was examined versus the solution accuracy. Next, the optimized strain sensor position derived from simulation analysis was applied to instruct experiments for the three-dimensional deformation monitoring problems under the concentrated loads, and the effectiveness of the method was demonstrated by comparing the reconstructed results with those of dial indicator set. Additionally, the large-scale model testing system was experimentally applied to monitor and analyze the deformation behavior of pipeline structures crossing a strike-slip fault. Subsequently, these dense strain measurements from distributed optical fiber sensors were used to perform iFEM analysis, which utilized various discrete elements and strain sensor configurations. Overall, experimental results demonstrate that this method facilitates localization of the three-dimensional pipeline deformations, aiding pipeline condition assessment and exhibiting the potential for practical applications.
{"title":"Experimental study on three-dimensional deformation reconstruction of pipelines using the inverse finite element method","authors":"Yuanju Wang, Runzhou You, Liang Ren, Xin Feng, Jiang Cui","doi":"10.1016/j.measurement.2026.120659","DOIUrl":"10.1016/j.measurement.2026.120659","url":null,"abstract":"<div><div>Pipeline deformation monitoring plays a crucial role in ensuring the safety of oil and gas transportation. The inverse finite element method (iFEM) is an emerging deformation reconstruction approach that can be implemented without invoking force equilibrium. While limited experimental work is available for three-dimensional deformation reconstruction of pipelines. To overcome this limitation, this paper proposes an integrated approach combining the inverse finite element method (iFEM) with FBG/DOFS-based strain sensing technology for three-dimensional deformation reconstruction of pipelines. Two experimental studies were conducted, encompassing the three-dimensional deformation evaluative testing and the large-scale model testing. During the three-dimensional deformation evaluative testing, a numerical model was established, and the effect of strain sensor location and discrete element level was examined versus the solution accuracy. Next, the optimized strain sensor position derived from simulation analysis was applied to instruct experiments for the three-dimensional deformation monitoring problems under the concentrated loads, and the effectiveness of the method was demonstrated by comparing the reconstructed results with those of dial indicator set. Additionally, the large-scale model testing system was experimentally applied to monitor and analyze the deformation behavior of pipeline structures crossing a strike-slip fault. Subsequently, these dense strain measurements from distributed optical fiber sensors were used to perform iFEM analysis, which utilized various discrete elements and strain sensor configurations. Overall, experimental results demonstrate that this method facilitates localization of the three-dimensional pipeline deformations, aiding pipeline condition assessment and exhibiting the potential for practical applications.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"268 ","pages":"Article 120659"},"PeriodicalIF":5.6,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-31DOI: 10.1016/j.measurement.2026.120644
Yanlin Li , Na Wei , Guo Chen , Chuang Shi , Jingnan Liu
Realizing a Terrestrial Reference Frame (TRF) with an accuracy of 1 mm and a long-term stability of 0.1 mm/yr is a longstanding goal of the geodesy field. To achieve this, selecting an appropriate stochastic model to accurately characterize the nonlinear coordinate variations of geodetic stations is essential for TRF realization. However, the commonly used Random Walk (RW) model in filtering is not the optimal noise model for time-correlated noise in Global Navigation Satellite System (GNSS) coordinates. In this study, we replace the RW model with a first-order autoregressive (AR[1]) process to model the GNSS time-correlated noise and implement a GNSS TRF solution aligned with ITRF2020 via the Square Root Information Filter (SRIF). We found that the AR[1] process used in this study has a higher cut-off frequency than the RW model, allowing it to retain a larger portion of the input flicker noise. Consequently, the GNSS time-correlated noise modelled by AR[1] more closely approximates true flicker noise than that modelled by RW. When time-correlated noise is modelled by AR[1], the median RMS of coordinate residuals is decreases to 0.3 and 2.0 mm in the horizontal and up components, respectively. Moreover, the AR[1] process can capture short-term correlations in time-correlated noise parameters, thereby enhancing the accuracy of short-term (approximately 11 weeks) TRF coordinate predictions. These findings demonstrate the potential of incorporating time-correlated noise using AR[1] in GNSS data assimilation, with implications for both multi-technique global TRF realization and regional GNSS TRF solutions.
{"title":"Improved time-correlated noise modeling for GNSS terrestrial reference frame realization via square root information filter","authors":"Yanlin Li , Na Wei , Guo Chen , Chuang Shi , Jingnan Liu","doi":"10.1016/j.measurement.2026.120644","DOIUrl":"10.1016/j.measurement.2026.120644","url":null,"abstract":"<div><div>Realizing a Terrestrial Reference Frame (TRF) with an accuracy of 1 mm and a long-term stability of 0.1 mm/yr is a longstanding goal of the geodesy field. To achieve this, selecting an appropriate stochastic model to accurately characterize the nonlinear coordinate variations of geodetic stations is essential for TRF realization. However, the commonly used Random Walk (RW) model in filtering is not the optimal noise model for time-correlated noise in Global Navigation Satellite System (GNSS) coordinates. In this study, we replace the RW model with a first-order autoregressive (AR[1]) process to model the GNSS time-correlated noise and implement a GNSS TRF solution aligned with ITRF2020 via the Square Root Information Filter (SRIF). We found that the AR[1] process used in this study has a higher cut-off frequency than the RW model, allowing it to retain a larger portion of the input flicker noise. Consequently, the GNSS time-correlated noise modelled by AR[1] more closely approximates true flicker noise than that modelled by RW. When time-correlated noise is modelled by AR[1], the median RMS of coordinate residuals is decreases to 0.3 and 2.0 mm in the horizontal and up components, respectively. Moreover, the AR[1] process can capture short-term correlations in time-correlated noise parameters, thereby enhancing the accuracy of short-term (approximately 11 weeks) TRF coordinate predictions. These findings demonstrate the potential of incorporating time-correlated noise using AR[1] in GNSS data assimilation, with implications for both multi-technique global TRF realization and regional GNSS TRF solutions.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"268 ","pages":"Article 120644"},"PeriodicalIF":5.6,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172086","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-31DOI: 10.1016/j.measurement.2026.120645
Chen Ren , Chen Wang , Zhenhong Li , Mingrui Yang , Haoran Gong
While satellite laser ranging (SLR) has long been recognized as a valuable complement to microwave tracking for GNSS precise orbit determination, previous studies have largely examined individual modeling aspects in isolation. A systematic investigation of how SLR prior precision, range-bias (RB) parameterization, and their prior constraints jointly affect BDS-3 orbit determination and geodetic parameters is still lacking. Using four years of BDS-3 microwave and SLR observations, this study systematically assesses the impact of different SLR–BDS-3 fusion strategies on precise orbit determination, geocenter motion, and Earth rotation parameters (ERPs). We identify an reliable strategy (denoted as M1C5) in which station–satellite RB are estimated together with prior sigmas of 1 cm for SLR observations and 5 cm for RB. This strategy yields stable and significant improvements in orbit accuracy and geocenter motion compared to the microwave-only solution. The contribution of SLR to radial orbit accuracy is strongly anti-correlated with the density of the microwave tracking network; when the global network expands to 40 stations, the additional SLR-induced improvement is reduced to about 0.59%. Spectral analysis of the geocenter time series further indicates that inappropriate RB modeling amplifies spurious short-period noise, including an artificial 7-day signal and other short-period oscillations, whereas the M1C5 strategy effectively suppresses these artifacts. Moreover, incorporating SLR enhances the recovery of annual and sub-annual geocenter harmonics in the X, Y, and Z components. In contrast, the impact of SLR on ERP estimation is marginal and not systematic under the current data volume and network geometry.
{"title":"The contribution of satellite laser ranging to the BDS-3 constellation: precise orbit determination and geodetic parameters estimation","authors":"Chen Ren , Chen Wang , Zhenhong Li , Mingrui Yang , Haoran Gong","doi":"10.1016/j.measurement.2026.120645","DOIUrl":"10.1016/j.measurement.2026.120645","url":null,"abstract":"<div><div>While satellite laser ranging (SLR) has long been recognized as a valuable complement to microwave tracking for GNSS precise orbit determination, previous studies have largely examined individual modeling aspects in isolation. A systematic investigation of how SLR prior precision, range-bias (RB) parameterization, and their prior constraints jointly affect BDS-3 orbit determination and geodetic parameters is still lacking. Using four years of BDS-3 microwave and SLR observations, this study systematically assesses the impact of different SLR–BDS-3 fusion strategies on precise orbit determination, geocenter motion, and Earth rotation parameters (ERPs). We identify an reliable strategy (denoted as M1C5) in which station–satellite RB are estimated together with prior sigmas of 1 cm for SLR observations and 5 cm for RB. This strategy yields stable and significant improvements in orbit accuracy and geocenter motion compared to the microwave-only solution. The contribution of SLR to radial orbit accuracy is strongly anti-correlated with the density of the microwave tracking network; when the global network expands to 40 stations, the additional SLR-induced improvement is reduced to about 0.59%. Spectral analysis of the geocenter time series further indicates that inappropriate RB modeling amplifies spurious short-period noise, including an artificial 7-day signal and other short-period oscillations, whereas the M1C5 strategy effectively suppresses these artifacts. Moreover, incorporating SLR enhances the recovery of annual and sub-annual geocenter harmonics in the X, Y, and Z components. In contrast, the impact of SLR on ERP estimation is marginal and not systematic under the current data volume and network geometry.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"268 ","pages":"Article 120645"},"PeriodicalIF":5.6,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172040","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-31DOI: 10.1016/j.measurement.2026.120677
Zhanpeng Gao , Jun Liu , Wenjun Yi , Shusen Yuan , Jun Guan
Addressing the challenge of insufficient accuracy in time-to-go () estimation for missiles in complex combat environments is critical. While existing physical model-based methods are computationally efficient, they often struggle to effectively cope with high-dynamic and highly uncertain battlefield conditions. To overcome this limitation, this paper proposes a hybrid error compensation model that employs the Harris Hawks Optimization (HHO) algorithm to optimize the Extreme Gradient Boosting (XGBoost) framework. This method utilizes HHO-XGBoost to learn the nonlinear deviation between the physical analytical solution and the actual flight time, providing real-time compensation to the physical model. Simulation results demonstrate that the proposed model exhibits superior accuracy: in stationary target engagement missions, the maximum prediction error is merely 0.45 s; in more complex moving target scenarios, the maximum error is controlled within 0.27 s, significantly outperforming existing comparative models. Furthermore, the model maintains high prediction stability under varying degrees of observation errors, verifying its strong robustness. Application of this algorithm to an Impact Time Control guidance law reveals that high-precision estimation not only ensures precise target impact at the desired time but also effectively avoids acceleration saturation caused by estimation deviations during the initial guidance phase, thereby significantly enhancing the overall stability of the guidance process
{"title":"Missile remaining flight time estimation via physics-guided residual learning","authors":"Zhanpeng Gao , Jun Liu , Wenjun Yi , Shusen Yuan , Jun Guan","doi":"10.1016/j.measurement.2026.120677","DOIUrl":"10.1016/j.measurement.2026.120677","url":null,"abstract":"<div><div>Addressing the challenge of insufficient accuracy in time-to-go (<span><math><msub><mrow><mi>t</mi></mrow><mrow><mi>g</mi><mi>o</mi></mrow></msub></math></span>) estimation for missiles in complex combat environments is critical. While existing physical model-based methods are computationally efficient, they often struggle to effectively cope with high-dynamic and highly uncertain battlefield conditions. To overcome this limitation, this paper proposes a hybrid error compensation model that employs the Harris Hawks Optimization (HHO) algorithm to optimize the Extreme Gradient Boosting (XGBoost) framework. This method utilizes HHO-XGBoost to learn the nonlinear deviation between the physical analytical solution and the actual flight time, providing real-time compensation to the physical model. Simulation results demonstrate that the proposed model exhibits superior accuracy: in stationary target engagement missions, the maximum prediction error is merely 0.45 s; in more complex moving target scenarios, the maximum error is controlled within 0.27 s, significantly outperforming existing comparative models. Furthermore, the model maintains high prediction stability under varying degrees of observation errors, verifying its strong robustness. Application of this algorithm to an Impact Time Control guidance law reveals that high-precision <span><math><msub><mrow><mi>t</mi></mrow><mrow><mi>g</mi><mi>o</mi></mrow></msub></math></span> estimation not only ensures precise target impact at the desired time but also effectively avoids acceleration saturation caused by estimation deviations during the initial guidance phase, thereby significantly enhancing the overall stability of the guidance process</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"268 ","pages":"Article 120677"},"PeriodicalIF":5.6,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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