Machine learning (ML) has been used for predicting pile base resistance, yet prediction for long piles remains difficult because significant base resistance is mobilized only after a certain degree of settlement. This study proposes a hybrid framework that combines sparse Bayesian learning (SBL) for probabilistic prediction with a Tent Chaotic Gaussian Sparrow Search Algorithm (TCGSSA) for hyperparameter tuning under cross-validation. Model performance and uncertainty quantification are evaluated using point and interval metrics on field data from 37 projects in Ho Chi Minh City, Vietnam. This study applies the maximum information coefficient (MIC) as a data-level diagnostic to quantify input–output associations and to check consistency with established geotechnical understanding. The framework delivers accurate point predictions together with sharp, well-covered intervals and compares favorably with the baselines considered. The diagnostics indicate that the displacement at the point of loading exerts the greatest influence on base resistance among the variables examined. The approach provides a mechanism-consistent, uncertainty-quantified tool for the design and assessment of long piles in soft soils.
{"title":"Mechanism-consistent probabilistic model for base resistance of long piles in soft soil using optimized sparse bayesian learning and factor correlation analysis","authors":"Kailiang Weng , Mincai Jia , Gang Zhang , Qingyuan Zeng","doi":"10.1016/j.measurement.2026.120639","DOIUrl":"10.1016/j.measurement.2026.120639","url":null,"abstract":"<div><div>Machine learning (ML) has been used for predicting pile base resistance, yet prediction for long piles remains difficult because significant base resistance is mobilized only after a certain degree of settlement. This study proposes a hybrid framework that combines sparse Bayesian learning (SBL) for probabilistic prediction with a Tent Chaotic Gaussian Sparrow Search Algorithm (TCGSSA) for hyperparameter tuning under cross-validation. Model performance and uncertainty quantification are evaluated using point and interval metrics on field data from 37 projects in Ho Chi Minh City, Vietnam. This study applies the maximum information coefficient (MIC) as a data-level diagnostic to quantify input–output associations and to check consistency with established geotechnical understanding. The framework delivers accurate point predictions together with sharp, well-covered intervals and compares favorably with the baselines considered. The diagnostics indicate that the displacement at the point of loading exerts the greatest influence on base resistance among the variables examined. The approach provides a mechanism-consistent, uncertainty-quantified tool for the design and assessment of long piles in soft soils.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"268 ","pages":"Article 120639"},"PeriodicalIF":5.6,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146171991","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 : 2026-01-29DOI: 10.1016/j.measurement.2026.120633
Kah Hong Lee , Norhisham Bakhary , Khairul H. Padil , Jun Li , Yon Kong Chen
The effectiveness of vibration-based damage detection depends heavily on the accuracy and completeness of the measured data. However, data loss is inevitable in structural health monitoring, making data reconstruction crucial to ensuring structural safety. However, revealing all complex correlations between the input and the output data in machine learning approaches remains a challenge. Beyond that, even though it is known that spatiotemporal, auto-temporal, and temperature data is influential to the fluctuations of acceleration data, using all three correlations in machine-learning approaches simultaneously remains a bottleneck. To address these challenges, this study presents a novel approach for dynamic response data recovery employing a Cauchy–Schwarz variational autoencoder with a hybrid data arrangement model called the spatial-auto-temporal thermal consistency model. The model uses a probabilistic encoder-decoder structure that leverages the rich expressiveness of a mixed Gaussian as the latent representation to reveal complex relationships between input and output data. The unique architecture of the deep learning model also enables it to be trained using spatiotemporal, auto-temporal, and temperature dependencies simultaneously in the SATTC configuration. The effectiveness of the proposed approach is demonstrated through a case study of field data from the Guangzhou New TV Tower (GNTT). The effects of input channels and measurement noise are also investigated. The quantitative analysis and modal identification results indicate that the proposed approach yields more accurate data reconstruction than VAE-ST, and GAN-ST, and slightly more accurate than CSVAE-ST.
{"title":"Advanced data reconstruction of vibration signals using a Cauchy–Schwarz variational autoencoder with a spatial-auto-temporal thermal consistency model","authors":"Kah Hong Lee , Norhisham Bakhary , Khairul H. Padil , Jun Li , Yon Kong Chen","doi":"10.1016/j.measurement.2026.120633","DOIUrl":"10.1016/j.measurement.2026.120633","url":null,"abstract":"<div><div>The effectiveness of vibration-based damage detection depends heavily on the accuracy and completeness of the measured data. However, data loss is inevitable in structural health monitoring, making data reconstruction crucial to ensuring structural safety. However, revealing all complex correlations between the input and the output data in machine learning approaches remains a challenge. Beyond that, even though it is known that spatiotemporal, auto-temporal, and temperature data is influential to the fluctuations of acceleration data, using all three correlations in machine-learning approaches simultaneously remains a bottleneck. To address these challenges, this study presents a novel approach for dynamic response data recovery employing a Cauchy–Schwarz variational autoencoder with a hybrid data arrangement model called the spatial-auto-temporal thermal consistency model. The model uses a probabilistic encoder-decoder structure that leverages the rich expressiveness of a mixed Gaussian as the latent representation to reveal complex relationships between input and output data. The unique architecture of the deep learning model also enables it to be trained using spatiotemporal, auto-temporal, and temperature dependencies simultaneously in the SATTC configuration. The effectiveness of the proposed approach is demonstrated through a case study of field data from the Guangzhou New TV Tower (GNTT). The effects of input channels and measurement noise are also investigated. The quantitative analysis and modal identification results indicate that the proposed approach yields more accurate data reconstruction than VAE-ST, and GAN-ST, and slightly more accurate than CSVAE-ST.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"267 ","pages":"Article 120633"},"PeriodicalIF":5.6,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146191093","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 : 2026-01-29DOI: 10.1016/j.measurement.2026.120638
Jing Feng , Yu Tian , Xiaolian Liu , Zhaolong Xie , Hao Wang
To address fault signal recognition challenges in pumping station units under high noise and small sample conditions, this study proposes a measurement method based on Hierarchical Multiscale Attention Entropy (HMATE), a novel complexity index. HMATE, a quantifiable and interpretable measurand for non-stationary signal complexity, is integrated with attention-weighted entropy in the wavelet multiscale domain, enhancing noise resistance and stability. The HMATE results are visualized through Symmetrized Dot Pattern (SDP) and classified using ResNet50, achieving accurate identification of multiple fault types. Experimental results show over 95% accuracy under four types of strong noise and high reliability with small samples. t-SNE visualization confirms distinct separability of operational states. This study provides an effective fault diagnosis solution and introduces a metrologically grounded method for precise measurement of non-stationary signals.
{"title":"Hierarchical multiscale attention entropy-based fault identification for SDP images and ResNet of water diversion pumping station","authors":"Jing Feng , Yu Tian , Xiaolian Liu , Zhaolong Xie , Hao Wang","doi":"10.1016/j.measurement.2026.120638","DOIUrl":"10.1016/j.measurement.2026.120638","url":null,"abstract":"<div><div>To address fault signal recognition challenges in pumping station units under high noise and small sample conditions, this study proposes a measurement method based on Hierarchical Multiscale Attention Entropy (HMATE), a novel complexity index. HMATE, a quantifiable and interpretable measurand for non-stationary signal complexity, is integrated with attention-weighted entropy in the wavelet multiscale domain, enhancing noise resistance and stability. The HMATE results are visualized through Symmetrized Dot Pattern (SDP) and classified using ResNet50, achieving accurate identification of multiple fault types. Experimental results show over 95% accuracy under four types of strong noise and high reliability with small samples. t-SNE visualization confirms distinct separability of operational states. This study provides an effective fault diagnosis solution and introduces a metrologically grounded method for precise measurement of non-stationary signals.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"268 ","pages":"Article 120638"},"PeriodicalIF":5.6,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146096028","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}
To address the issues of poor noise suppression in strong noise environments and the difficulty in preserving fault characteristics, this paper proposes an efficient denoising method combining CEEMDAN-PSO-TV (complete ensemble empirical mode decomposition with adaptive noise particle swarm optimization total variation)combined with AWR (adaptive weighting reconstruction) and DRC (dynamic residual compensation). First, the bearing vibration signal is decomposed using CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise) to obtain a series of IMF (intrinsic mode function) components. The variance contribution rate of each IMF is calculated, and IMFs with a rate exceeding 5% are retained, while the remaining IMFs are filtered out. Second, each selected IMF undergoes denoising using the TV (total variation) algorithm. The PSO (particle swarm optimization) algorithm is incorporated during this process to adaptively select the regularization parameter λ of the TV algorithm for each selected IMF. Multiple initialization strategy enhances the PSO algorithm’s ability to find the global optimum, thereby enabling the adaptive selection of the optimal λ parameter for each IMF. Third, the denoised IMFs undergo AWR, DRC is then applied to the reconstructed signal to enhance both the denoising effectiveness and fault feature retention capability of the bearing vibration signal, ultimately yielding the final denoised signal. The proposed method was applied to both simulated and actual bearing vibration signals, and its performance was compared against multiple denoising methods. The results demonstrate that the denoising method based on CEEMDAN-PSO-TV combined AWR and DRC significantly outperforms existing methods in both noise suppression and fault feature retention for bearing vibration signals, thereby providing a robust foundation for accurate fault feature extraction.
{"title":"A novel denoising method for bearing vibration signals in rotating machinery based on CEEMDAN-PSO-TV combined AWR and DRC","authors":"Wenkai Yong , Yulong Li , Lijun Yu , Xiaogang Zhang","doi":"10.1016/j.measurement.2026.120641","DOIUrl":"10.1016/j.measurement.2026.120641","url":null,"abstract":"<div><div>To address the issues of poor noise suppression in strong noise environments and the difficulty in preserving fault characteristics, this paper proposes an efficient denoising method combining CEEMDAN-PSO-TV (complete ensemble empirical mode decomposition with adaptive noise particle swarm optimization total variation)combined with AWR (adaptive weighting reconstruction) and DRC (dynamic residual compensation). First, the bearing vibration signal is decomposed using CEEMDAN (complete ensemble empirical mode decomposition with adaptive noise) to obtain a series of IMF (intrinsic mode function) components. The variance contribution rate of each IMF is calculated, and IMFs with a rate exceeding 5% are retained, while the remaining IMFs are filtered out. Second, each selected IMF undergoes denoising using the TV (total variation) algorithm. The PSO (particle swarm optimization) algorithm is incorporated during this process to adaptively select the regularization parameter <em>λ</em> of the TV algorithm for each selected IMF. Multiple initialization strategy enhances the PSO algorithm’s ability to find the global optimum, thereby enabling the adaptive selection of the optimal <em>λ</em> parameter for each IMF. Third, the denoised IMFs undergo AWR, DRC is then applied to the reconstructed signal to enhance both the denoising effectiveness and fault feature retention capability of the bearing vibration signal, ultimately yielding the final denoised signal. The proposed method was applied to both simulated and actual bearing vibration signals, and its performance was compared against multiple denoising methods. The results demonstrate that the denoising method based on CEEMDAN-PSO-TV combined AWR and DRC significantly outperforms existing methods in both noise suppression and fault feature retention for bearing vibration signals, thereby providing a robust foundation for accurate fault feature extraction.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"268 ","pages":"Article 120641"},"PeriodicalIF":5.6,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146096173","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 : 2026-01-29DOI: 10.1016/j.measurement.2026.120611
Zhaoyu Wen , Mingming He , Yangping Yao , Haoteng Wang
The in-situ acquisition of deep rock mechanical properties remains a major challenge in underground engineering. In this study, a prediction model for triaxial compressive strength, cohesion, and internal friction angle was developed by incorporating confining-pressure effects. The model was validated using drilling experiments on four rock types under five confining pressures, demonstrating high accuracy and reliability. The relationships between drilling parameters—such as torque and thrust—and the torque–thrust slope in the frictional stage were examined. The results indicate that these parameters exhibit linear positive correlations with confining pressure. The predicted parameters enabled continuous reconstruction of the spatial distributions of rock strength, cohesion, and internal friction angle, with accuracies generally within 25%. The study further reveals the nonlinear evolution of deep rock strength, characterized by increasing cohesion and decreasing friction angle with depth. Overall, this work provides a new technical pathway for the in-situ mechanical characterization of deep formations and supports intelligent, real-time drilling decision-making.
{"title":"Research on real-time inversion method of rock strength parameters while drilling considering confining pressure effect","authors":"Zhaoyu Wen , Mingming He , Yangping Yao , Haoteng Wang","doi":"10.1016/j.measurement.2026.120611","DOIUrl":"10.1016/j.measurement.2026.120611","url":null,"abstract":"<div><div>The in-situ acquisition of deep rock mechanical properties remains a major challenge in underground engineering. In this study, a prediction model for triaxial compressive strength, cohesion, and internal friction angle was developed by incorporating confining-pressure effects. The model was validated using drilling experiments on four rock types under five confining pressures, demonstrating high accuracy and reliability. The relationships between drilling parameters—such as torque and thrust—and the torque–thrust slope in the frictional stage were examined. The results indicate that these parameters exhibit linear positive correlations with confining pressure. The predicted parameters enabled continuous reconstruction of the spatial distributions of rock strength, cohesion, and internal friction angle, with accuracies generally within 25%. The study further reveals the nonlinear evolution of deep rock strength, characterized by increasing cohesion and decreasing friction angle with depth. Overall, this work provides a new technical pathway for the in-situ mechanical characterization of deep formations and supports intelligent, real-time drilling decision-making.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"268 ","pages":"Article 120611"},"PeriodicalIF":5.6,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172036","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 : 2026-01-29DOI: 10.1016/j.measurement.2026.120640
Alessandro Sardellitti , Filippo Milano , Vincenzo Mottola , Luigi Ferrigno , Antonello Tamburrino , Marco Laracca
This paper presents a dual-stage method for simultaneously estimating electrical conductivity, lift-off, and thickness in conductive samples using Eddy Current Testing (ECT) and dimensional analysis. The approach takes advantage of Buckingham’s theorem to reformulate the physical model in a reduced set of dimensionless variables, allowing for a computationally efficient and geometrically intuitive inversion based on intersections of level curves.
The proposed method integrates these principles into a two-step estimation process that requires only one high-frequency and one low-frequency measurement. In the first stage, a high-frequency thickness-independent measurement is used for the simultaneous estimation of electrical conductivity and lift-off. In the second stage, a low-frequency measurement is used to recover the remaining thickness through one of the three proposed strategies, depending on which parameters of Stage 1 are retained.
The method was experimentally validated on six conductive samples under five lift-off conditions. The results show high accuracy, with relative errors below 2.5% for both electrical conductivity and lift-off, and 3.2% for thickness, and excellent repeatability with standard deviations generally lower than 1%. The observed repeatability in both stages supports implementation in either a multi-frequency or a single-frequency configuration, enabling simplified hardware, reduced measurement time, and real-time applicability. Overall, the methodology represents a practical and flexible framework suitable for integration into modern industrial ECT systems and Industry 4.0 quality-control environments.
{"title":"A dual-stage dimensionless method for simultaneous estimation of electrical conductivity, lift-off, and thickness in Eddy Current Testing","authors":"Alessandro Sardellitti , Filippo Milano , Vincenzo Mottola , Luigi Ferrigno , Antonello Tamburrino , Marco Laracca","doi":"10.1016/j.measurement.2026.120640","DOIUrl":"10.1016/j.measurement.2026.120640","url":null,"abstract":"<div><div>This paper presents a dual-stage method for simultaneously estimating electrical conductivity, lift-off, and thickness in conductive samples using Eddy Current Testing (ECT) and dimensional analysis. The approach takes advantage of Buckingham’s <span><math><mi>π</mi></math></span> theorem to reformulate the physical model in a reduced set of dimensionless variables, allowing for a computationally efficient and geometrically intuitive inversion based on intersections of level curves.</div><div>The proposed method integrates these principles into a two-step estimation process that requires only one high-frequency and one low-frequency measurement. In the first stage, a high-frequency thickness-independent measurement is used for the simultaneous estimation of electrical conductivity and lift-off. In the second stage, a low-frequency measurement is used to recover the remaining thickness through one of the three proposed strategies, depending on which parameters of Stage 1 are retained.</div><div>The method was experimentally validated on six conductive samples under five lift-off conditions. The results show high accuracy, with relative errors below 2.5% for both electrical conductivity and lift-off, and 3.2% for thickness, and excellent repeatability with standard deviations generally lower than 1%. The observed repeatability in both stages supports implementation in either a multi-frequency or a single-frequency configuration, enabling simplified hardware, reduced measurement time, and real-time applicability. Overall, the methodology represents a practical and flexible framework suitable for integration into modern industrial ECT systems and Industry 4.0 quality-control environments.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"267 ","pages":"Article 120640"},"PeriodicalIF":5.6,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190445","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 : 2026-01-29DOI: 10.1016/j.measurement.2026.120626
Diogo Gonçalves , Gil Gonçalves , Maria da Conceição Cunha , Umberto Andriolo
The complex geometry of coastal cliffs poses challenges for achieving complete and detailed 3D reconstruction by Uncrewed Aerial Vehicle (UAV)-based imagery. This study proposes a novel four-step framework to optimise the UAV image acquisition geometry and flight path. It introduces curvature-based surface simplification and a linear quality metric to guide viewpoint selection, while guaranteeing consistent pixel size across images.
The optimised flight path was verified in the field. Only 2% of the candidate viewpoints comprised the optimised set, with an 84% reduction in flight duration. The acquired images generated a 3D point cloud that captured the entire cliff face, with a mean surface density of 3076 points per m2. In addition, acquiring images with a consistent ground sample distance across pixels improved the performance of Structure from Motion and Multi-View Stereo techniques in terms of geometric quality and the surface density of the 3D point cloud.
This study demonstrates that optimising UAV flight paths allows for faster and more effective surveys of complex coastal environments. It promotes the use of remote sensing technologies for cliff inspection and monitoring, thereby contributing for risk assessment and coastal management.
沿海悬崖的复杂几何形状为实现基于无人机（UAV）的图像的完整和详细的3D重建提出了挑战。该研究提出了一种新的四步框架来优化无人机图像采集几何形状和飞行路径。它引入了基于曲率的表面简化和线性质量度量来指导视点选择，同时保证了图像之间像素大小的一致。优化后的飞行路径在现场进行了验证。只有2%的候选视点构成了优化集，飞行时间减少了84%。获取的图像生成了一个3D点云，它捕获了整个悬崖表面，平均表面密度为每平方米3076个点。此外，从几何质量和三维点云的表面密度方面来看，获取具有一致的地面样本距离的图像可以改善Structure from Motion和Multi-View Stereo技术的性能。这项研究表明，优化无人机飞行路径可以更快、更有效地调查复杂的沿海环境。它促进使用遥感技术进行悬崖检查和监测，从而有助于风险评估和沿海管理。
{"title":"Optimising image acquisition geometry by uncrewed aerial vehicles: A framework for 3D reconstruction of coastal cliffs","authors":"Diogo Gonçalves , Gil Gonçalves , Maria da Conceição Cunha , Umberto Andriolo","doi":"10.1016/j.measurement.2026.120626","DOIUrl":"10.1016/j.measurement.2026.120626","url":null,"abstract":"<div><div>The complex geometry of coastal cliffs poses challenges for achieving complete and detailed 3D reconstruction by Uncrewed Aerial Vehicle (UAV)-based imagery. This study proposes a novel four-step framework to optimise the UAV image acquisition geometry and flight path. It introduces curvature-based surface simplification and a linear quality metric to guide viewpoint selection, while guaranteeing consistent pixel size across images.</div><div>The optimised flight path was verified in the field. Only 2% of the candidate viewpoints comprised the optimised set, with an 84% reduction in flight duration. The acquired images generated a 3D point cloud that captured the entire cliff face, with a mean surface density of 3076 points per m<sup>2</sup>. In addition, acquiring images with a consistent ground sample distance across pixels improved the performance of Structure from Motion and Multi-View Stereo techniques in terms of geometric quality and the surface density of the 3D point cloud.</div><div>This study demonstrates that optimising UAV flight paths allows for faster and more effective surveys of complex coastal environments. It promotes the use of remote sensing technologies for cliff inspection and monitoring, thereby contributing for risk assessment and coastal management.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"268 ","pages":"Article 120626"},"PeriodicalIF":5.6,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172039","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 : 2026-01-29DOI: 10.1016/j.measurement.2026.120606
Karim Emara , Ahmed Mahfouz M. M. Abd-Elgawad , Mohamed S. Gad , Ahmed Emara
The supremacy of magnetic field impact on spray and flame characteristics of traditional, renewable, and alternative liquid fuels has been rarely investigated particularly with annular air shields. Premeasurements are conducted like Fourier Transform Infrared Spectroscopy, Gas Chromatography-Mass Spectrometry, to characterize the physical and chemical properties for tested fuels. These measurements are carried out to characterize each fuel type and investigating the effect of changing some parameters. The current measurement’s purpose to address this research gap measurements for light diesel oil (LDO), waste cooking oil, waste tire pyrolysis oil, and their blends with the former (5% and 10% on a mass basis). The impact of magnetic field on flow field were conducted using the particle image velocimetry laser system under stagnant atmospheric conditions. Thermal and visual flame measurements are directed to visualize and predict flame temperatures. The findings emphasize that Ionization, de-clustering, and realignment will therefore result in an increase in the kinetic energy of the free electrons in the LDO fuel. Those phenomena, which include ionization and de-clustering, will speed up the vaporization and combining of oxygen atoms with the ionized hydrocarbon chain of LDO fuel to obtain a greater reaction rate during the combustion process, hence raising the combustion enthalpy. The magnetic field speeds up the droplets, facilitating better penetration into the combustion space and enhancing overall combustion efficiency. Moreover, the magnetic field would lower the system’s initial cost and enhance power consumption.
{"title":"High precision measurement and monitoring of the magnetic dominance and annular air shield impact on thermal energy and spray flow field for a coaxial liquid burner","authors":"Karim Emara , Ahmed Mahfouz M. M. Abd-Elgawad , Mohamed S. Gad , Ahmed Emara","doi":"10.1016/j.measurement.2026.120606","DOIUrl":"10.1016/j.measurement.2026.120606","url":null,"abstract":"<div><div>The supremacy of<!--> <!-->magnetic field impact on spray and flame characteristics of traditional, renewable, and alternative liquid fuels has been rarely investigated particularly with annular air shields. Premeasurements are conducted like Fourier Transform Infrared Spectroscopy, Gas Chromatography-Mass Spectrometry, to characterize the physical and chemical properties for tested fuels. These measurements are carried out to characterize each fuel type and investigating the effect of changing some parameters. The current measurement’s purpose to address this research gap measurements for light diesel oil (LDO), waste cooking oil, waste tire pyrolysis oil, and their blends<!--> <!-->with the former (5% and 10% on a mass basis). The impact of magnetic field on flow field were conducted using<!--> <!-->the particle image velocimetry laser system<!--> <!-->under stagnant atmospheric conditions. Thermal and visual flame measurements are directed to visualize and predict flame temperatures. The findings emphasize that Ionization, de-clustering, and realignment will therefore result in an increase in the kinetic energy of the free electrons in the LDO fuel. Those phenomena, which include ionization and de-clustering, will speed up the vaporization and combining of oxygen atoms with the ionized hydrocarbon chain of LDO fuel to obtain a greater reaction rate during the combustion process, hence raising the combustion enthalpy. The magnetic field speeds up the droplets, facilitating better penetration into the combustion space and enhancing overall combustion efficiency. Moreover, the magnetic field would lower the system’s initial cost and enhance power consumption.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"267 ","pages":"Article 120606"},"PeriodicalIF":5.6,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146190444","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 : 2026-01-29DOI: 10.1016/j.measurement.2026.120635
Bo Zhang , Zexiao Li , Weisheng Cheng, Xiaodong Zhang
To address the demand for 6-DOF pose measurement of multi-planar composite material micro-components in high-precision Linear Tape-Open (LTO) storage technology, this paper proposes a pose measurement method based on four-dimensional (4D) data fusion. A system integrating high-precision 3D measurement sensors with precision motion control axes was designed and constructed, enabling precision recognition of component feature regions by fusing 3D point cloud data with optical signals. Furthermore, a standard template matching model based on nonlinear optimization algorithms was developed to effectively solve the challenge of precise alignment of multiple-planar components (MPC) under 6-DOF. Experimental results demonstrate that this method achieves stable and high-precision measurements in complex environments, with a maximum standard deviation of relative displacement error in the most significant direction at 1.5 μm, the minimum standard deviation in other directions at 20 nm, and a maximum angular deviation standard deviation of less than 0.05°. This approach enables the differentiation and measurement of composite materials, meets the stringent requirements of industrial multi-component precision measurement, and provides critical technical support for enhancing the reliability and performance of Linear Tape-Open (LTO) storage systems.
{"title":"High-precision positioning of LTO magnetic track by four-dimensional topographic measurement","authors":"Bo Zhang , Zexiao Li , Weisheng Cheng, Xiaodong Zhang","doi":"10.1016/j.measurement.2026.120635","DOIUrl":"10.1016/j.measurement.2026.120635","url":null,"abstract":"<div><div>To address the demand for 6-DOF pose measurement of multi-planar composite material micro-components in high-precision Linear Tape-Open (LTO) storage technology, this paper proposes a pose measurement method based on four-dimensional (4D) data fusion. A system integrating high-precision 3D measurement sensors with precision motion control axes was designed and constructed, enabling precision recognition of component feature regions by fusing 3D point cloud data with optical signals. Furthermore, a standard template matching model based on nonlinear optimization algorithms was developed to effectively solve the challenge of precise alignment of multiple-planar components (MPC) under 6-DOF. Experimental results demonstrate that this method achieves stable and high-precision measurements in complex environments, with a maximum standard deviation of relative displacement error in the most significant direction at 1.5 μm, the minimum standard deviation in other directions at 20 nm, and a maximum angular deviation standard deviation of less than 0.05°. This approach enables the differentiation and measurement of composite materials, meets the stringent requirements of industrial multi-component precision measurement, and provides critical technical support for enhancing the reliability and performance of Linear Tape-Open (LTO) storage systems.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"268 ","pages":"Article 120635"},"PeriodicalIF":5.6,"publicationDate":"2026-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146172087","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 : 2026-01-29DOI: 10.1016/j.measurement.2026.120600
Le Hao , Xiaowei Zhai , Kai Wang , Jun Li , Qingjie Zeng
Gas leak monitoring represents a critical component in the production, transportation, and processing of high-sulfur natural gas, playing a vital role in ensuring operational safety across all stages and enabling environmental impact assessment following potential leaks. This study addresses spectral interference challenges in mid-infrared laser gas monitoring systems by developing a gas concentration inversion model based on a mixed-Lorentzian approach. Focusing on the two primary constituents of high-sulfur natural gas − methane (CH4) and hydrogen sulfide (H2S) − we established an 8.309 μm central spectral line suitable for simultaneous detection of both gases and implemented a remote mid-infrared laser system.To resolve signal interference between CH4 and H2S during mixed-gas monitoring, we employed spectral line broadening techniques under simulated high-sulfur gas leakage conditions. This enabled effective deployment of the mixed-Lorentzian model for gas signal separation. The parameters derived from the separated Lorentzian components were subsequently integrated into our concentration inversion model, achieving successful decomposition of mixed infrared laser signals.System stability evaluations demonstrated that our mixed-Lorentzian separation model effectively resolves composite gas signals while preserving absorption feature integrity. The model achieved correlation coefficients of 0.9541 for CH4 and 0.9591 for H2S, both exceeding the 0.95 threshold. These results confirm the method’s accuracy in simultaneous monitoring of CH4 and H2S concentrations within high-sulfur natural gas environments. This methodology shows significant potential for extension to similar challenges across the energy sector.
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