Pub Date : 2026-02-04DOI: 10.1016/j.measurement.2026.120523
Yi Chengshan , Wang Hong , Li Jing , Xie Hongtai
The rapid evolution of the high-speed rail network and EMU technology impose increasingly stringent requirements for maintenance and support systems. Due to the limited availability of fault samples, some components still suffer from improper maintenance cycles and over- or under-maintenance. To address the ineffective integration between condition monitoring and scheduled maintenance, we first construct a WPHM-based dynamic Condition-Based Maintenance (CBM) timing decision model targeting maximum availability by incorporating RAMS indicators and using historical gearbox condition monitoring data as the input. Maintainability is quantified via Analytic Hierarchy Process (AHP) to formulate maintainability loss cost; Game Theory is employed in conjunction with Fuzzy AHP (FAHP) and Entropy Weight Method (EWM) to determine weights, transforming safety into safety risk cost. We then develop an integrated dynamic maintenance optimization model combining CBM with scheduled preventive maintenance (PM), targeting the minimum cost under reliability and availability constraints. The results show a 9.53% cost reduction and improved real-time tracking of operational and health conditions. Our research provides meaningful references for advancing the modernization of railway O&M systems toward intelligent operation, encompassing condition sensing, prediction, and smart decision-making.
{"title":"Rams-based modeling and decision optimization for condition-based maintenance strategies of EMU components","authors":"Yi Chengshan , Wang Hong , Li Jing , Xie Hongtai","doi":"10.1016/j.measurement.2026.120523","DOIUrl":"10.1016/j.measurement.2026.120523","url":null,"abstract":"<div><div>The rapid evolution of the high-speed rail network and EMU technology impose increasingly stringent requirements for maintenance and support systems. Due to the limited availability of fault samples, some components still suffer from improper maintenance cycles and over- or under-maintenance. To address the ineffective integration between condition monitoring and scheduled maintenance, we first construct a WPHM-based dynamic Condition-Based Maintenance (CBM) timing decision model targeting maximum availability by incorporating RAMS indicators and using historical gearbox condition monitoring data as the input. Maintainability is quantified via Analytic Hierarchy Process (AHP) to formulate maintainability loss cost; Game Theory is employed in conjunction with Fuzzy AHP (FAHP) and Entropy Weight Method (EWM) to determine weights, transforming safety into safety risk cost. We then develop an integrated dynamic maintenance optimization model combining CBM with scheduled preventive maintenance (PM), targeting the minimum cost under reliability and availability constraints. The results show a 9.53% cost reduction and improved real-time tracking of operational and health conditions. Our research provides meaningful references for advancing the modernization of railway O&M systems toward intelligent operation, encompassing condition sensing, prediction, and smart decision-making.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"268 ","pages":"Article 120523"},"PeriodicalIF":5.6,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171929","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 : 2026-02-04DOI: 10.1016/j.measurement.2026.120711
Mengyao Fan , Huining Zhao , Yu He , Minghui Duan , Haojie Xia
To address the challenges of enhancing on-site positioning accuracy and calibration measurement for robots, this paper proposes a robot calibration and measurement method that integrates binocular vision with cross-structured light measurement technology. This method leverages the unique advantages of binocular vision in three-dimensional perception and the high-precision characteristics of cross-structured light in feature extraction, aiming to significantly improve the positioning accuracy of the robot and the measurement accuracy after calibration. Firstly, the binocular vision cross-structured light measurement system is installed at the robot end-effector to conduct error modeling of the entire system. Secondly, the robot, equipped with the binocular vision cross-structured light measurement system, measures the 3D artifact. An objective function is established by calculating the root mean square error between the measured values and the standard values. Thirdly, algorithms are employed to iteratively minimize the objective function, thereby obtaining the robot’s kinematic parameter errors, which are then compensated to enhance the positioning accuracy of the robot. Finally, specific experimental verifications are conducted. Initially, the cross-structured light measurement system is tested to confirm its accuracy and stability. Subsequently, robot calibration, validation and measurement experiments are performed. The results of the robot verification experiment indicate that the mean positioning error decreases from 0.964 mm to 0.135 mm after calibration. Similarly, in the standard step block measurement experiment, the mean measurement errors for step blocks 1 and 2 decreased from 0.379 mm and 0.245 mm to 0.172 mm and 0.121 mm, respectively, after robot calibration. These experiments validate the accuracy and reliability of the proposed method, demonstrating its applicability for robot calibration and measurement tasks.
{"title":"Robot calibration and measurement method based on binocular vision and cross-structured light system","authors":"Mengyao Fan , Huining Zhao , Yu He , Minghui Duan , Haojie Xia","doi":"10.1016/j.measurement.2026.120711","DOIUrl":"10.1016/j.measurement.2026.120711","url":null,"abstract":"<div><div>To address the challenges of enhancing on-site positioning accuracy and calibration measurement for robots, this paper proposes a robot calibration and measurement method that integrates binocular vision with cross-structured light measurement technology. This method leverages the unique advantages of binocular vision in three-dimensional perception and the high-precision characteristics of cross-structured light in feature extraction, aiming to significantly improve the positioning accuracy of the robot and the measurement accuracy after calibration. Firstly, the binocular vision cross-structured light measurement system is installed at the robot end-effector to conduct error modeling of the entire system. Secondly, the robot, equipped with the binocular vision cross-structured light measurement system, measures the 3D artifact. An objective function is established by calculating the root mean square error between the measured values and the standard values. Thirdly, algorithms are employed to iteratively minimize the objective function, thereby obtaining the robot’s kinematic parameter errors, which are then compensated to enhance the positioning accuracy of the robot. Finally, specific experimental verifications are conducted. Initially, the cross-structured light measurement system is tested to confirm its accuracy and stability. Subsequently, robot calibration, validation and measurement experiments are performed. The results of the robot verification experiment indicate that the mean positioning error decreases from 0.964 mm to 0.135 mm after calibration. Similarly, in the standard step block measurement experiment, the mean measurement errors for step blocks 1 and 2 decreased from 0.379 mm and 0.245 mm to 0.172 mm and 0.121 mm, respectively, after robot calibration. These experiments validate the accuracy and reliability of the proposed method, demonstrating its applicability for robot calibration and measurement tasks.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"269 ","pages":"Article 120711"},"PeriodicalIF":5.6,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147306","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 : 2026-02-04DOI: 10.1016/j.measurement.2026.120703
Yan Shi , Wei Si , Yongping Hu , Pinxue Zhao , Rui Ma , Rui Liu , Gaer Awang Danzeng , Biao Ma
Reliable evaluation of asphalt mixture compaction quality is critical for ensuring pavement performance and durability. This study proposes a novel non-destructive compaction prediction framework that integrates Ground Penetrating Radar (GPR) with machine learning (ML) algorithms to assess asphalt compaction quality. The proposed framework establishes a complete and systematic prediction pipeline linking compaction parameters, dielectric response, and compaction state, in which compaction parameters, including temperature, number of roller passes, and mixture gradation, are first mapped to GPR-derived dielectric constants and subsequently converted into the corresponding compaction degree. Laboratory experiments involving four asphalt mixture types (AC-13, AC-20, ATB-25, OGFC-13) demonstrated strong correlations between dielectric constants and compaction indices, with correlation coefficients ranging from 0.69 to 0.85. Based on this quantitative relationship, four machine learning models, namely multilayer perceptron (MLP), random forest (RF), support vector machine (SVM), and Gaussian process regression (GausPR), were developed to predict dielectric constants from compaction parameters. Among them, the SVM model achieved the highest predictive accuracy, with correlation coefficients of 0.996 for dense-graded mixtures and 0.995 for open-graded mixtures. Full-scale slab compaction experiments further confirmed the reliability and spatial adaptability of the proposed framework, with average prediction errors remaining around 1.0 percent for dense-graded mixtures and exhibiting strong spatial consistency. The results demonstrate that the proposed GPR and ML framework enables non-destructive, high-resolution, and continuous real-time monitoring of asphalt compaction quality, providing practical value for intelligent construction and quality control in asphalt pavement engineering.
{"title":"Predicting asphalt mixture compaction quality using ground penetrating radar and machine learning","authors":"Yan Shi , Wei Si , Yongping Hu , Pinxue Zhao , Rui Ma , Rui Liu , Gaer Awang Danzeng , Biao Ma","doi":"10.1016/j.measurement.2026.120703","DOIUrl":"10.1016/j.measurement.2026.120703","url":null,"abstract":"<div><div>Reliable evaluation of asphalt mixture compaction quality is critical for ensuring pavement performance and durability. This study proposes a novel non-destructive compaction prediction framework that integrates Ground Penetrating Radar (GPR) with machine learning (ML) algorithms to assess asphalt compaction quality. The proposed framework establishes a complete and systematic prediction pipeline linking compaction parameters, dielectric response, and compaction state, in which compaction parameters, including temperature, number of roller passes, and mixture gradation, are first mapped to GPR-derived dielectric constants and subsequently converted into the corresponding compaction degree. Laboratory experiments involving four asphalt mixture types (AC-13, AC-20, ATB-25, OGFC-13) demonstrated strong correlations between dielectric constants and compaction indices, with correlation coefficients ranging from 0.69 to 0.85. Based on this quantitative relationship, four machine learning models, namely multilayer perceptron (MLP), random forest (RF), support vector machine (SVM), and Gaussian process regression (GausPR), were developed to predict dielectric constants from compaction parameters. Among them, the SVM model achieved the highest predictive accuracy, with correlation coefficients of 0.996 for dense-graded mixtures and 0.995 for open-graded mixtures. Full-scale slab compaction experiments further confirmed the reliability and spatial adaptability of the proposed framework, with average prediction errors remaining around 1.0 percent for dense-graded mixtures and exhibiting strong spatial consistency. The results demonstrate that the proposed GPR and ML framework enables non-destructive, high-resolution, and continuous real-time monitoring of asphalt compaction quality, providing practical value for intelligent construction and quality control in asphalt pavement engineering.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"268 ","pages":"Article 120703"},"PeriodicalIF":5.6,"publicationDate":"2026-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172120","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 : 2026-02-03DOI: 10.1016/j.measurement.2026.120689
Mehmet Ünal , Mustafa Özcanlı
This study explores the acoustic performance of a novel hybrid composite reinforced with natural fibers, specifically Luffa cylindrica and Coconut fibers, in the context of automotive noise insulation. The goal was to replace conventional diesel engine lids with eco-friendly composite alternatives. Samples with varying fiber ratios were produced using the hand lay-up method and tested via impedance tube to evaluate their sound absorption coefficient (SAC) and sound transmission loss (STL). Based on laboratory results, the optimal composition was determined to be 75% Coconut and 25% Luffa fiber by volume, as tested using impedance tube measurements (ASTM E2611, ASTM E1050, ISO 10534–2). Subsequently, these findings were applied in full-scale vehicle testing, including articulation index and decibel level measurements, using the same composite configuration. The composite lid incorporating the optimal ratio improved the interior noise environment in the driver area by up to 7% in articulation index, as confirmed by tests at three different speeds. All reported data are directly linked to the configurations and test methods described in the manuscript. The material’s cost-effectiveness is supported by using low-cost, renewable fibers and simple processes. Unlike many previous studies that remain at component-level characterization, this work demonstrates an in-vehicle application and acoustic validation of a natural fiber hybrid composite and correlates standardized impedance tube results with full-scale vehicle measurements.
本研究探讨了一种新型的混杂复合材料增强天然纤维，特别是丝瓜和椰子纤维，在汽车隔音的背景下的声学性能。其目标是用环保的复合材料替代传统的柴油发动机盖。采用手铺法制备不同纤维比的样品，并通过阻抗管测试其吸声系数（SAC）和传声损失（STL）。根据实验室结果，通过阻抗管测量（ASTM E2611, ASTM E1050, ISO 10534-2），确定最佳成分为75%椰子纤维和25%丝瓜纤维（按体积计）。随后，这些结果被应用于全尺寸车辆测试，包括发音指数和分贝水平测量，使用相同的复合材料配置。在三种不同速度的测试中证实，采用最佳比例的复合车盖可将驾驶员区域的内部噪声环境的清晰度指数提高7%。所有报告的数据都与论文中描述的配置和测试方法直接相关。这种材料的成本效益得益于使用低成本、可再生纤维和简单的工艺。与之前许多停留在部件级表征的研究不同，这项工作展示了天然纤维混合复合材料的车内应用和声学验证，并将标准化阻抗管结果与全尺寸车辆测量相关联。
{"title":"Vehicle integrated acoustic evaluation of eco-friendly hybrid composites reinforced with Luffa and Coconut fibers","authors":"Mehmet Ünal , Mustafa Özcanlı","doi":"10.1016/j.measurement.2026.120689","DOIUrl":"10.1016/j.measurement.2026.120689","url":null,"abstract":"<div><div>This study explores the acoustic performance of a novel hybrid composite reinforced with natural fibers, specifically Luffa cylindrica and Coconut fibers, in the context of automotive noise insulation. The goal was to replace conventional diesel engine lids with eco-friendly composite alternatives. Samples with varying fiber ratios were produced using the hand lay-up method and tested via impedance tube to evaluate their sound absorption coefficient (SAC) and sound transmission loss (STL). Based on laboratory results, the optimal composition was determined to be 75% Coconut and 25% Luffa fiber by volume, as tested using impedance tube measurements (ASTM <span><span>E2611</span><svg><path></path></svg></span>, ASTM E1050, ISO 10534–2). Subsequently, these findings were applied in full-scale vehicle testing, including articulation index and decibel level measurements, using the same composite configuration. The composite lid incorporating the optimal ratio improved the interior noise environment in the driver area by up to 7% in articulation index, as confirmed by tests at three different speeds. All reported data are directly linked to the configurations and test methods described in the manuscript. The material’s cost-effectiveness is supported by using low-cost, renewable fibers and simple processes. Unlike many previous studies that remain at component-level characterization, this work demonstrates an in-vehicle application and acoustic validation of a natural fiber hybrid composite and correlates standardized impedance tube results with full-scale vehicle measurements.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"268 ","pages":"Article 120689"},"PeriodicalIF":5.6,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172119","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 : 2026-02-03DOI: 10.1016/j.measurement.2026.120694
Göknur Berber Narin , Muhammet Vefa Akpınar
Road traffic noise is a significant environmental concern that affects urban livability and public health. Conventional prediction models generally assume linear sound propagation, which often leads to inaccuracies under real field conditions. This study aims to investigate the actual propagation behavior of road traffic noise in both horizontal and vertical directions, and to test the validity of the linear assumption using a multi-point, multi-level field measurement approach.
Noise data were simultaneously collected at nine points over five different days under varying meteorological and traffic conditions. Equivalent continuous sound levels (LAeq) were analyzed, and contour maps were generated using dBmap.net in accordance with ISO 9613-2 standards.
The results reveal that noise attenuation follows a non-linear pattern with horizontal distance (≈0.21 dBA/m), while levels increase with height (≈0.68 dBA/m). Additionally, slopes and wind conditions were found to influence propagation significantly. These findings indicate that the widely accepted linear assumption in existing models, such as CNOSSOS-EU and FHWA TNM, does not fully reflect real-world conditions.
The study highlights the necessity of three-dimensional analysis to improve the accuracy of road traffic noise modeling and control strategies.
{"title":"Revisiting road traffic noise propagation: evidence of non-linear behavior from multi-point field measurements","authors":"Göknur Berber Narin , Muhammet Vefa Akpınar","doi":"10.1016/j.measurement.2026.120694","DOIUrl":"10.1016/j.measurement.2026.120694","url":null,"abstract":"<div><div>Road traffic noise is a significant environmental concern that affects urban livability and public health. Conventional prediction models generally assume linear sound propagation, which often leads to inaccuracies under real field conditions. This study aims to investigate the actual propagation behavior of road traffic noise in both horizontal and vertical directions, and to test the validity of the linear assumption using a multi-point, multi-level field measurement approach.</div><div>Noise data were simultaneously collected at nine points over five different days under varying meteorological and traffic conditions. Equivalent continuous sound levels (L<sub>Aeq</sub>) were analyzed, and contour maps were generated using dBmap.net in accordance with ISO 9613-2 standards.</div><div>The results reveal that noise attenuation follows a non-linear pattern with horizontal distance (≈0.21 dBA/m), while levels increase with height (≈0.68 dBA/m). Additionally, slopes and wind conditions were found to influence propagation significantly. These findings indicate that the widely accepted linear assumption in existing models, such as CNOSSOS-EU and FHWA TNM, does not fully reflect real-world conditions.</div><div>The study highlights the necessity of three-dimensional analysis to improve the accuracy of road traffic noise modeling and control strategies.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"269 ","pages":"Article 120694"},"PeriodicalIF":5.6,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147309","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 : 2026-02-03DOI: 10.1016/j.measurement.2026.120696
Wufa Long , Jianhua Zheng , Shaoxin Li , Zhijie Luo
In practical applications of digital microfluidic (DMF) chips, structural faults such as short or open circuits in the electrode system can be promptly detected through online testing, whereby test droplets operate concurrently during experiments. This capability substantially enhances the reliability of experimental procedures. To further enhance the efficiency of online testing in DMF chips, we propose a parallel multi-droplet testing strategy that minimizes redundant fluidic conflicts and ensures experimental accuracy. In addition, we introduce a single-droplet path-splitting strategy, which alleviates the complexity of fluidic constraints during multi-droplet testing. An improved genetic algorithm, integrated with a heuristic addressing mechanism and an elitist strategy, is employed to conduct parallel multi-droplet online testing on DMF chips of three different sizes. Simulation results demonstrate that, compared with the single-droplet testing approach, the proposed method achieves average optimization efficiencies of 66.1% with three droplets and 49.2% with two droplets. These results confirm that the proposed framework significantly enhances the efficiency of online testing in DMF chips.
{"title":"From splitting to synergy: parallel testing of digital microfluidic chips","authors":"Wufa Long , Jianhua Zheng , Shaoxin Li , Zhijie Luo","doi":"10.1016/j.measurement.2026.120696","DOIUrl":"10.1016/j.measurement.2026.120696","url":null,"abstract":"<div><div>In practical applications of digital microfluidic (DMF) chips, structural faults such as short or open circuits in the electrode system can be promptly detected through online testing, whereby test droplets operate concurrently during experiments. This capability substantially enhances the reliability of experimental procedures. To further enhance the efficiency of online testing in DMF chips, we propose a parallel multi-droplet testing strategy that minimizes redundant fluidic conflicts and ensures experimental accuracy. In addition, we introduce a single-droplet path-splitting strategy, which alleviates the complexity of fluidic constraints during multi-droplet testing. An improved genetic algorithm, integrated with a heuristic addressing mechanism and an elitist strategy, is employed to conduct parallel multi-droplet online testing on DMF chips of three different sizes. Simulation results demonstrate that, compared with the single-droplet testing approach, the proposed method achieves average optimization efficiencies of 66.1% with three droplets and 49.2% with two droplets. These results confirm that the proposed framework significantly enhances the efficiency of online testing in DMF chips.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"268 ","pages":"Article 120696"},"PeriodicalIF":5.6,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172079","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}
Mechanical equipment fault diagnosis faces the critical challenge of data scarcity in industrial scenarios. Addressing the limitations of traditional deep learning in capturing non-stationary and heterogeneous fault patterns under limited samples, this study proposes a few-shot fault diagnosis method based on Multi-domain Adaptive Collaborative Network (MACo-Net). Our model leverages a metric-based meta-learning paradigm to learn an embedding space from complementary time–frequency, angular, and recurrence domains. To overcome the generalization bottlenecks of static convolutional kernels, we introduce an adaptive feature prompt generator, which constructs a sample-adaptive feature space to capture non-stationary fault signatures. Subsequently, to resolve feature misalignment and detail loss, cross-domain collaborative attention is utilized to enforce semantic alignment across heterogeneous physical domains, and a multi-level progressive enhancement strategy is developed to hierarchically integrate shallow details with deep semantics. Experimental results demonstrate that the proposed method achieves diagnostic accuracies of 93.8% and 98.5% on the BJTU and WHU datasets, respectively. This research establishes a novel multi-domain fusion paradigm for data-scarce fault diagnosis.
{"title":"MACo-Net: a multi-domain adaptive collaborative network for few-shot fault diagnosis","authors":"Wanting Jing, Dezheng Wang, Qian Zhang, Congyan Chen","doi":"10.1016/j.measurement.2026.120691","DOIUrl":"10.1016/j.measurement.2026.120691","url":null,"abstract":"<div><div>Mechanical equipment fault diagnosis faces the critical challenge of data scarcity in industrial scenarios. Addressing the limitations of traditional deep learning in capturing non-stationary and heterogeneous fault patterns under limited samples, this study proposes a few-shot fault diagnosis method based on Multi-domain Adaptive Collaborative Network (MACo-Net). Our model leverages a metric-based <em>meta</em>-learning paradigm to learn an embedding space from complementary time–frequency, angular, and recurrence domains. To overcome the generalization bottlenecks of static convolutional kernels, we introduce an adaptive feature prompt generator, which constructs a sample-adaptive feature space to capture non-stationary fault signatures. Subsequently, to resolve feature misalignment and detail loss, cross-domain collaborative attention is utilized to enforce semantic alignment across heterogeneous physical domains, and a multi-level progressive enhancement strategy is developed to hierarchically integrate shallow details with deep semantics. Experimental results demonstrate that the proposed method achieves diagnostic accuracies of 93.8% and 98.5% on the BJTU and WHU datasets, respectively. This research establishes a novel multi-domain fusion paradigm for data-scarce fault diagnosis.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"268 ","pages":"Article 120691"},"PeriodicalIF":5.6,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172084","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}
Vision-based methods provide efficient solutions and vital information for measurement of structures while facing challenges under noise interference and in spatial measurement. To this end, a novel accurate monocular structure pose estimation and 3D trajectory measurement method with single square marker is proposed, addressing the incomplete imaging weakness and lack of robustness in spatial localization. The proposed method integrates corner extraction, efficient center localization, and the weighted reprojection optimization method to estimate the six degrees of freedom (6-Dof) pose of the small-size target with automation. Simulation tests with various noise levels, experimental tests mixing multi-type of spatial motions and real scene tests are conducted to evaluate the proposed method. Furthermore, the proposed workflow enables dense point cloud reconstruction from single image stream, bringing vision-based measurement with power as LiDAR sensing. Despite the target-to-distance ratio of 60 mm: 1600 mm, experimental results show the method achieves accuracy as fine as 0.01 mm with low-scale upscaling, offering an accurate and low-cost solution for non-contact 6-DoF maintenance of structure.
{"title":"Accurate monocular structure pose estimation and 3D trajectory measurement with single square marker","authors":"Sicheng Hong , Yuyong Xiong , Yingjie Gou , Shaohan Chen , Wendi Tian , Zhike Peng","doi":"10.1016/j.measurement.2026.120685","DOIUrl":"10.1016/j.measurement.2026.120685","url":null,"abstract":"<div><div>Vision-based methods provide efficient solutions and vital information for measurement of structures while facing challenges under noise interference and in spatial measurement. To this end, a novel accurate monocular structure pose estimation and 3D trajectory measurement method with single square marker is proposed, addressing the incomplete imaging weakness and lack of robustness in spatial localization. The proposed method integrates corner extraction, efficient center localization, and the weighted reprojection optimization method to estimate the six degrees of freedom (6-Dof) pose of the small-size target with automation. Simulation tests with various noise levels, experimental tests mixing multi-type of spatial motions and real scene tests are conducted to evaluate the proposed method. Furthermore, the proposed workflow enables dense point cloud reconstruction from single image stream, bringing vision-based measurement with power as LiDAR sensing. Despite the target-to-distance ratio of 60 mm: 1600 mm, experimental results show the method achieves accuracy as fine as 0.01 mm with low-scale upscaling, offering an accurate and low-cost solution for non-contact 6-DoF maintenance of structure.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"269 ","pages":"Article 120685"},"PeriodicalIF":5.6,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147308","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 : 2026-02-03DOI: 10.1016/j.measurement.2026.120700
Vimal Kumar Pathak , Vedant Singh , Tej Singh
The prairie dog optimization (PDO) algorithm has gained considerable interest among researchers and is continuously exercised to solve optimization problems across diverse domains. The PDO algorithm imitates unique foraging mechanism, burrow-construction behaviour of prairie dogs, and also emulates response actions to specific alarms for finding global optimal solutions in objective space across complex landscapes. The current study provides an extensive and structured review of the classical PDO algorithm, its statistical evaluation, improvements, and hybridizations with other nature-inspired algorithms, across different applications since its inception in 2022 to March 2025. The review also outlines a discussion on PDO advancements, highlighting its strengths and weaknesses for improving PDO performance further. The present investigation reveals that PDO related work have been published mostly in Springer (25) and Elsevier (21) publication houses. In addition, one of the key findings showed that PDO was mostly improved through hybridization with other nature inspire algorithms in 33 % studies followed by OBL based improvement in 9 % of PDO research. The analysis of published papers on classical PDO indicates that it is extensively used for addressing various optimization challenges in energy conservation (28 %) and electrical engineering (24 %) applications. Lastly, the PDO performance was statistically evaluated on standard benchmark functions, and concluding remarks are provided with future research avenues for the advancement of PDO.
{"title":"Advances in Prairie dog optimization algorithm: a comprehensive review","authors":"Vimal Kumar Pathak , Vedant Singh , Tej Singh","doi":"10.1016/j.measurement.2026.120700","DOIUrl":"10.1016/j.measurement.2026.120700","url":null,"abstract":"<div><div>The prairie dog optimization (PDO) algorithm has gained considerable interest among researchers and is continuously exercised to solve optimization problems across diverse domains. The PDO algorithm imitates unique foraging mechanism, burrow-construction behaviour of prairie dogs, and also emulates response actions to specific alarms for finding global optimal solutions in objective space across complex landscapes. The current study provides an extensive and structured review of the classical PDO algorithm, its statistical evaluation, improvements, and hybridizations with other nature-inspired algorithms, across different applications since its inception in 2022 to March 2025. The review also outlines a discussion on PDO advancements, highlighting its strengths and weaknesses for improving PDO performance further. The present investigation reveals that PDO related work have been published mostly in Springer (25) and Elsevier (21) publication houses. In addition, one of the key findings showed that PDO was mostly improved through hybridization with other nature inspire algorithms in 33 % studies followed by OBL based improvement in 9 % of PDO research. The analysis of published papers on classical PDO indicates that it is extensively used for addressing various optimization challenges in energy conservation (28 %) and electrical engineering (24 %) applications. Lastly, the PDO performance was statistically evaluated on standard benchmark functions, and concluding remarks are provided with future research avenues for the advancement of PDO.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"269 ","pages":"Article 120700"},"PeriodicalIF":5.6,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147468","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 : 2026-02-03DOI: 10.1016/j.measurement.2026.120686
Saihan Chen , Peng Liu , Haixu Yang , Huanyu Zhang , Wei Luo , Peng Wang , Yuting Luo
The surface strain of lithium-ion batteries (LIBs) is a critical signal for accurate internal state estimation and safety monitoring. However, a unified understanding of strain evolution for different cell formats is still lacking. In this investigation, fiber Bragg grating sensors were employed to acquire decoupled measurements of surface strain and temperature in commercial pouch and prismatic LIBs. Static experiments under controlled state of charge (SOC) and temperature conditions enabled a systematic analysis of surface strain characteristics, revealing the underlying response mechanisms for each cell type. Notably, under SOC-induced variations, the side-surface strain of prismatic cells evolves opposite to the cell’s volumetric change and exhibits a strong negative correlation with SOC, a phenomenon reported here for the first time. Cycling experiments further provided dynamic strain data, and a strain composition model was developed to quantitatively assess the contributions of SOC, temperature, and inconsistency-induced strain to total dynamic strain. Finally, the stage-wise evolution of dynamic strain was analyzed, elucidating the dominant mechanisms at each stage. This work provides both theoretical and experimental guidance for optimizing surface strain measurement in commercial LIBs and lays the foundation for high-precision, mechanically informed state estimation and intelligent fault diagnosis in engineering applications.
{"title":"Experimental investigation of surface strain behavior in commercial pouch and prismatic Lithium-Ion batteries","authors":"Saihan Chen , Peng Liu , Haixu Yang , Huanyu Zhang , Wei Luo , Peng Wang , Yuting Luo","doi":"10.1016/j.measurement.2026.120686","DOIUrl":"10.1016/j.measurement.2026.120686","url":null,"abstract":"<div><div>The surface strain of lithium-ion batteries (LIBs) is a critical signal for accurate internal state estimation and safety monitoring. However, a unified understanding of strain evolution for different cell formats is still lacking. In this investigation, fiber Bragg grating sensors were employed to acquire decoupled measurements of surface strain and temperature in commercial pouch and prismatic LIBs. Static experiments under controlled state of charge (SOC) and temperature conditions enabled a systematic analysis of surface strain characteristics, revealing the underlying response mechanisms for each cell type. Notably, under SOC-induced variations, the side-surface strain of prismatic cells evolves opposite to the cell’s volumetric change and exhibits a strong negative correlation with SOC, a phenomenon reported here for the first time. Cycling experiments further provided dynamic strain data, and a strain composition model was developed to quantitatively assess the contributions of SOC, temperature, and inconsistency-induced strain to total dynamic strain. Finally, the stage-wise evolution of dynamic strain was analyzed, elucidating the dominant mechanisms at each stage. This work provides both theoretical and experimental guidance for optimizing surface strain measurement in commercial LIBs and lays the foundation for high-precision, mechanically informed state estimation and intelligent fault diagnosis in engineering applications.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"268 ","pages":"Article 120686"},"PeriodicalIF":5.6,"publicationDate":"2026-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172115","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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