Pub Date : 2026-01-13DOI: 10.1016/j.measurement.2026.120443
Haotian Li , Hongtu Cheng , Congdong She , Jianghong Zhang , Xi Zhu , Fuping Zeng , Zhi Fang
The transition metals in gas-insulated switchgear (GIS) significantly accelerate SF6 decomposition at thermal fault temperatures. Yet, the exploration of the reaction mechanism at SF6/Cu and SF6/Ag interfaces remains insufficiently characterized with limited kinetic data. Based on density functional theory (DFT) and transition state theory (TST), we mapped comprehensive potential energy surfaces (PES) for SF6-metal interfacial reactions. The energy barriers for SF6 dissociation were reduced to 0.29 eV on Cu(111) and 0.19 eV on Ag(111) surfaces, respectively. Further charge density difference (CDD) and partial density of states (PDOS) analyses revealed the electronic origins: enhanced F-2p/Cu-3d hybridization facilitates charge transfer on Cu, whereas Ag’s narrower bonding-antibonding gap yields the lower dissociation barrier. Kinetic analysis was performed at 200 ∼ 800 K, demonstrating higher rate coefficients for SF6 dissociation on the Ag surface. To verify the calculations, we conducted heating experiments with quantitative gas-phase measurements, confirming the superior reaction activity of silver electrodes with 30% higher SF6 conversion than copper. The insights into gas-surface interactions explain the SF6 stability failure mechanisms, and this work provides a reference for corrosion protection of metal components such as Cu/Ag contacts in GIS.
{"title":"Unravelling the mechanism of SF6/Cu and SF6/Ag interactions at elevated temperatures via DFT calculations and kinetic analysis","authors":"Haotian Li , Hongtu Cheng , Congdong She , Jianghong Zhang , Xi Zhu , Fuping Zeng , Zhi Fang","doi":"10.1016/j.measurement.2026.120443","DOIUrl":"10.1016/j.measurement.2026.120443","url":null,"abstract":"<div><div>The transition metals in gas-insulated switchgear (GIS) significantly accelerate SF<sub>6</sub> decomposition at thermal fault temperatures. Yet, the exploration of the reaction mechanism at SF<sub>6</sub>/Cu and SF<sub>6</sub>/Ag interfaces remains insufficiently characterized with limited kinetic data. Based on density functional theory (DFT) and transition state theory (TST), we mapped comprehensive potential energy surfaces (PES) for SF<sub>6</sub>-metal interfacial reactions. The energy barriers for SF<sub>6</sub> dissociation were reduced to 0.29 eV on Cu(111) and 0.19 eV on Ag(111) surfaces, respectively. Further charge density difference (CDD) and partial density of states (PDOS) analyses revealed the electronic origins: enhanced F-2p/Cu-3d hybridization facilitates charge transfer on Cu, whereas Ag’s narrower bonding-antibonding gap yields the lower dissociation barrier. Kinetic analysis was performed at 200 ∼ 800 K, demonstrating higher rate coefficients for SF<sub>6</sub> dissociation on the Ag surface. To verify the calculations, we conducted heating experiments with quantitative gas-phase measurements, confirming the superior reaction activity of silver electrodes with 30% higher SF<sub>6</sub> conversion than copper. The insights into gas-surface interactions explain the SF<sub>6</sub> stability failure mechanisms, and this work provides a reference for corrosion protection of metal components such as Cu/Ag contacts in GIS.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"266 ","pages":"Article 120443"},"PeriodicalIF":5.6,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145969256","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 : 2026-01-13DOI: 10.1016/j.measurement.2026.120437
Priyanka Das, V. Nithin, A. Deepak, Keertana Sarvani Chilakapati
In this work, two different antennas working in an ultrawideband operating range [3–9 GHz] have been designed. A 1-dimensional UNet convolutional neural network (CNN) architecture is used to model and predict the S11 parameters of the first antenna geometry based on frequency inputs. The core algorithm is designed to learn complex nonlinear mappings between frequency sequences and associated antenna design parameters. A deterministic deep ensemble framework is developed to accurately model and predict the second antenna return loss parameter as a function of frequency and key geometric design parameters. The two antenna elements have been arranged orthogonally in a MIMO configuration for detecting the retention of water in knees caused due to rheumatoid arthritis from 3.2 to 9.6 GHz. Further, a four element heterogeneous MIMO antenna is designed with similar patches placed opposite to each other and common ground for operation from 13 to 50 GHz with mutual coupling lower than −25 dB throughout the band. The S parameters of the fabricated MIMO antenna are measured on a clay phantom with water-filled balloons placed inside it for creating an anomaly. Detection of anomaly by UNet CNN architecture is implemented for early diagnosis of rheumatoid arthritis from 13 to 50 GHz by extracting S parameters of the MIMO antenna sensors.
{"title":"Deep learning enabled heterogeneous MIMO antenna sensor for detection of rheumatoid arthritis","authors":"Priyanka Das, V. Nithin, A. Deepak, Keertana Sarvani Chilakapati","doi":"10.1016/j.measurement.2026.120437","DOIUrl":"10.1016/j.measurement.2026.120437","url":null,"abstract":"<div><div>In this work, two different antennas working in an ultrawideband operating range [3–9 GHz] have been designed. A 1-dimensional UNet convolutional neural network (CNN) architecture is used to model and predict the S<sub>11</sub> parameters of the first antenna geometry based on frequency inputs. The core algorithm is designed to learn complex nonlinear mappings between frequency sequences and associated antenna design parameters. A deterministic deep ensemble framework is developed to accurately model and predict the second antenna return loss parameter as a function of frequency and key geometric design parameters. The two antenna elements have been arranged orthogonally in a MIMO configuration for detecting the retention of water in knees caused due to rheumatoid arthritis from 3.2 to 9.6 GHz. Further, a four element heterogeneous MIMO antenna is designed with similar patches placed opposite to each other and common ground for operation from 13 to 50 GHz with mutual coupling lower than −25 dB throughout the band. The S parameters of the fabricated MIMO antenna are measured on a clay phantom with water-filled balloons placed inside it for creating an anomaly. Detection of anomaly by UNet CNN architecture is implemented for early diagnosis of rheumatoid arthritis from 13 to 50 GHz by extracting S parameters of the MIMO antenna sensors.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"265 ","pages":"Article 120437"},"PeriodicalIF":5.6,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979880","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 : 2026-01-13DOI: 10.1016/j.measurement.2026.120441
Artyom Movsisyan , Billi Minasyan , Hasmik Manukyan , Zhirayr Baghdasaryan , Kiejin Lee , Arsen Babajanyan
This study presents advanced optical indicators (OIs) based on functional metastructures (MSs) for thermoelastic optical indicator microscopy (TEOIM). The proposed MS-based OIs are designed to enhance microwave-induced thermoelastic response through geometry-controlled electromagnetic (EM) coupling, overcoming the limited sensitivity and isotropic behavior of conventional homogeneous indium tin oxide (ITO) indicators. By combining finite element simulations with experimental validation, we demonstrate that MS-based OIs provide significantly higher sensitivity and enable directional (anisotropic) detection of in-plane EM field components. Among the investigated designs, specific MSs exhibit either a strong isotropic thermal response or pronounced anisotropy, enabling selective visualization of vertical and horizontal field components. The effective spatial resolution of TEOIM is governed by thermoelastic stress distribution and optical readout rather than by the microwave wavelength, yielding an estimated resolution of approximately 150–250 nm under visible-light illumination. While the simulations employ simplified thermal boundary conditions and are intended to capture relative trends rather than absolute temperature values, the experimental results consistently confirm the enhanced sensitivity and directional selectivity introduced by MS geometry. These findings highlight the potential of MS-based OIs to improve microwave near-field visualization for metamaterial characterization, advanced sensor development, and biomedical sensing.
{"title":"Advanced optical indicators based on functional metastructure for thermoelastic optical indicator microscope","authors":"Artyom Movsisyan , Billi Minasyan , Hasmik Manukyan , Zhirayr Baghdasaryan , Kiejin Lee , Arsen Babajanyan","doi":"10.1016/j.measurement.2026.120441","DOIUrl":"10.1016/j.measurement.2026.120441","url":null,"abstract":"<div><div>This study presents advanced optical indicators (OIs) based on functional metastructures (MSs) for thermoelastic optical indicator microscopy (TEOIM). The proposed MS-based OIs are designed to enhance microwave-induced thermoelastic response through geometry-controlled electromagnetic (EM) coupling, overcoming the limited sensitivity and isotropic behavior of conventional homogeneous indium tin oxide (ITO) indicators. By combining finite element simulations with experimental validation, we demonstrate that MS-based OIs provide significantly higher sensitivity and enable directional (anisotropic) detection of in-plane EM field components. Among the investigated designs, specific MSs exhibit either a strong isotropic thermal response or pronounced anisotropy, enabling selective visualization of vertical and horizontal field components. The effective spatial resolution of TEOIM is governed by thermoelastic stress distribution and optical readout rather than by the microwave wavelength, yielding an estimated resolution of approximately 150–250 nm under visible-light illumination. While the simulations employ simplified thermal boundary conditions and are intended to capture relative trends rather than absolute temperature values, the experimental results consistently confirm the enhanced sensitivity and directional selectivity introduced by MS geometry. These findings highlight the potential of MS-based OIs to improve microwave near-field visualization for metamaterial characterization, advanced sensor development, and biomedical sensing.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"265 ","pages":"Article 120441"},"PeriodicalIF":5.6,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980601","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 : 2026-01-13DOI: 10.1016/j.measurement.2026.120438
Guosen Wang , Ying Su , Mengdi Wang , Xiaohui Luo , Junhua Hu , Weijie Shi
Hydraulic piezoelectric energy harvester (HPEH) can convert pressure pulsation vibration energy into electrical energy for use in powering low-power components. However, conventional HPEHs typically depend on tuning a single design parameter to enhance energy harvesting efficiency, significantly restricting their operational bandwidth. To address these limitations, this paper proposes an HPEH with dual-frequency regulation via integrated copper substrate and spring components, enabling broad control over natural frequency shifts. A theoretical model is developed and verified using a built experimental platform. Effects of copper substrate thickness, spring stiffness and load resistance on energy harvesting characteristics are studied, with optimization methods proposed for these parameters. Results indicate that the model aligns well with experiment across different structural parameters, frequencies and resistances. The combination of a 1.8 mm spring wire diameter and a 0.4 mm copper substrate thickness yielded the maximum bandwidth of 135 Hz. There exists an optimal resistance value for maximizing power and the optimal resistance decreases with increasing frequency. Increasing the copper substrate thickness elevates natural frequency but reduce peak voltage. Greater spring stiffness increases both natural frequency and peak voltage. The proposed parameter optimization method comprehensively considers factors such as pressure, copper substrate thickness and spring stiffness, providing a theoretical reference for the wideband frequency regulation of HPEH.
{"title":"Theoretical and experimental study of a piezoelectric energy harvester with dual-frequency regulation modes","authors":"Guosen Wang , Ying Su , Mengdi Wang , Xiaohui Luo , Junhua Hu , Weijie Shi","doi":"10.1016/j.measurement.2026.120438","DOIUrl":"10.1016/j.measurement.2026.120438","url":null,"abstract":"<div><div>Hydraulic piezoelectric energy harvester (HPEH) can convert pressure pulsation vibration energy into electrical energy for use in powering low-power components. However, conventional HPEHs typically depend on tuning a single design parameter to enhance energy harvesting efficiency, significantly restricting their operational bandwidth. To address these limitations, this paper proposes an HPEH with dual-frequency regulation via integrated copper substrate and spring components, enabling broad control over natural frequency shifts. A theoretical model is developed and verified using a built experimental platform. Effects of copper substrate thickness, spring stiffness and load resistance on energy harvesting characteristics are studied, with optimization methods proposed for these parameters. Results indicate that the model aligns well with experiment across different structural parameters, frequencies and resistances. The combination of a 1.8 mm spring wire diameter and a 0.4 mm copper substrate thickness yielded the maximum bandwidth of 135 Hz. There exists an optimal resistance value for maximizing power and the optimal resistance decreases with increasing frequency. Increasing the copper substrate thickness elevates natural frequency but reduce peak voltage. Greater spring stiffness increases both natural frequency and peak voltage. The proposed parameter optimization method comprehensively considers factors such as pressure, copper substrate thickness and spring stiffness, providing a theoretical reference for the wideband frequency regulation of HPEH.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"265 ","pages":"Article 120438"},"PeriodicalIF":5.6,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979821","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 : 2026-01-13DOI: 10.1016/j.measurement.2026.120447
Hangyu Zhong , Benyuan Sun
Deep learning strategies have shown great potential in addressing the highly ill-posed inverse problems in electrical impedance tomography (EIT). The success of neural network models is mainly attributed to their ability to extract valuable features from raw data. However, supervised learning often requires a complex training process and substantial data support, which poses constraints in practical physical applications. Meanwhile, although unsupervised learning shows promise in handling inverse problems, it typically comes with a high computational cost. To address these challenges, we propose applying the fine-tuning method to the neural network-based EIT inverse problem solving tasks. The key idea is to integrate data-driven and model-driven approaches. Calderón’s method provides the pre-trained network with an initial image, which is post-processed by the neural network to form an initial solution guess. The network parameters are subsequently optimized by a finite element module in conjunction with the network’s backpropagation to fine-tune the electrical conductivity distribution. The results demonstrate that this method significantly improves computational efficiency while maintaining excellent absolute imaging quality. Additionally, the improvements in complex scenarios and the effects of different hyperparameter settings are also thoroughly investigated to further validate the reliability and robustness of the proposed method.
{"title":"Fine-tuning deep Calderón for absolute imaging with electrical impedance tomography","authors":"Hangyu Zhong , Benyuan Sun","doi":"10.1016/j.measurement.2026.120447","DOIUrl":"10.1016/j.measurement.2026.120447","url":null,"abstract":"<div><div>Deep learning strategies have shown great potential in addressing the highly ill-posed inverse problems in electrical impedance tomography (EIT). The success of neural network models is mainly attributed to their ability to extract valuable features from raw data. However, supervised learning often requires a complex training process and substantial data support, which poses constraints in practical physical applications. Meanwhile, although unsupervised learning shows promise in handling inverse problems, it typically comes with a high computational cost. To address these challenges, we propose applying the fine-tuning method to the neural network-based EIT inverse problem solving tasks. The key idea is to integrate data-driven and model-driven approaches. Calderón’s method provides the pre-trained network with an initial image, which is post-processed by the neural network to form an initial solution guess. The network parameters are subsequently optimized by a finite element module in conjunction with the network’s backpropagation to fine-tune the electrical conductivity distribution. The results demonstrate that this method significantly improves computational efficiency while maintaining excellent absolute imaging quality. Additionally, the improvements in complex scenarios and the effects of different hyperparameter settings are also thoroughly investigated to further validate the reliability and robustness of the proposed method.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"265 ","pages":"Article 120447"},"PeriodicalIF":5.6,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145979816","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 : 2026-01-12DOI: 10.1016/j.measurement.2026.120407
Dongdong An , Zigeng Liu , Shengchun Liu , Wenda Gong , Ang Li
Underwater Acoustic Target Classification (UATC) is vital for enhancing underwater information gathering and facilitating effective countermeasures. However, a long-standing challenge in underwater acoustic target classification is mitigating the computational burden of feature fusion methods without compromising classification accuracy. Inspired by the efficiency of dolphin sonar, this study proposes a biomimetic classification approach that combines synchro-extracting transform features with dolphin auditory model. Nonlinear feature fusion is achieved using Kernel Canonical Correlation Analysis (KCCA), followed by classification through a Radial Basis Function Neural Network (RBFNN). The results of experiments demonstrate that the proposed method outperforms traditional fusion techniques, achieving an accuracy of 93.51%, which represents an improvement of 10.41% and 2.35% over CCA and PCA, respectively. Furthermore, in contrast to methods relying on a single feature extraction, the proposed approach achieves both higher recognition rates and stronger noise robustness. Meanwhile, it compresses the feature dimension to 1/6 of the original dimension, significantly reducing the computational complexity of classification models. This work presents an advancement in acoustic target classification, demonstrating the benefits of nonlinear feature fusion and the potential for improving underwater target recognition in underwater environment monitoring and shipwreck salvage.
{"title":"A biomimetic nonlinear feature fusion method for underwater acoustic target classification","authors":"Dongdong An , Zigeng Liu , Shengchun Liu , Wenda Gong , Ang Li","doi":"10.1016/j.measurement.2026.120407","DOIUrl":"10.1016/j.measurement.2026.120407","url":null,"abstract":"<div><div>Underwater Acoustic Target Classification (UATC) is vital for enhancing underwater information gathering and facilitating effective countermeasures. However, a long-standing challenge in underwater acoustic target classification is mitigating the computational burden of feature fusion methods without compromising classification accuracy. Inspired by the efficiency of dolphin sonar, this study proposes a biomimetic classification approach that combines synchro-extracting transform features with dolphin auditory model. Nonlinear feature fusion is achieved using Kernel Canonical Correlation Analysis (KCCA), followed by classification through a Radial Basis Function Neural Network (RBFNN). The results of experiments demonstrate that the proposed method outperforms traditional fusion techniques, achieving an accuracy of 93.51%, which represents an improvement of 10.41% and 2.35% over CCA and PCA, respectively. Furthermore, in contrast to methods relying on a single feature extraction, the proposed approach achieves both higher recognition rates and stronger noise robustness. Meanwhile, it compresses the feature dimension to 1/6 of the original dimension, significantly reducing the computational complexity of classification models. This work presents an advancement in acoustic target classification, demonstrating the benefits of nonlinear feature fusion and the potential for improving underwater target recognition in underwater environment monitoring and shipwreck salvage.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"266 ","pages":"Article 120407"},"PeriodicalIF":5.6,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145981980","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 : 2026-01-12DOI: 10.1016/j.measurement.2026.120416
Bemnet Wondimagegnehu Mersha , Yaping Dai , Kaoru Hirota
Fault detection is critical to ensure the safety of modern aerospace systems. Most existing studies evaluate data-driven fault detection methods against other data-driven fault methods and physics-based model fault detection methods against physics-based model fault detection methods. This compartmentalized evaluation impedes a comprehensive understanding of the strengths and limitations of each approach. To address this gap, we propose two novel physics-based models for the angle of attack (AoA) sensor fault detection: Manhattan Bias and Gain Tracking (MBGT) and Innovation Monitoring with Covariance Regularization (IMCR), both utilizing the Extended Kalman Filter (EKF). The proposed methods use a reduced order fixed-wing aircraft physics model developed using the first principles of physics and sensor fusion. We benchmarked these methods against machine learning-based approaches, including Long Short-Term Memory (LSTM) with residual analysis. The MBGT and IMCR are validated using flight data from the ATTAS research aircraft. The fault detection methods are evaluated under fault and fault-free conditions. Sensitivity analyses using a noisy sensor test dataset are also conducted. The results indicate that the MBGT and IMCR achieve near-zero false positive rates (FPR) under fault-free conditions. For ramp faults, the detection delays are 0.2 s for the IMCR and 0.18 s for the MBGT, demonstrating high responsiveness. In contrast, machine learning-based methods gave 0.4 s delay for ramp faults. Although physics-based methods are efficient and computationally lightweight, data-driven approaches, particularly LSTM, offer superior performance in noisy sensor environments and achieve lower FPR. The results show that a hybrid method is effective for fault detection.
{"title":"Physics-based fault detection for aircraft angle of attack sensors: Bias–gain tracking and covariance-regularized innovation monitoring evaluated against recurrent neural network-based fault detection methods","authors":"Bemnet Wondimagegnehu Mersha , Yaping Dai , Kaoru Hirota","doi":"10.1016/j.measurement.2026.120416","DOIUrl":"10.1016/j.measurement.2026.120416","url":null,"abstract":"<div><div>Fault detection is critical to ensure the safety of modern aerospace systems. Most existing studies evaluate data-driven fault detection methods against other data-driven fault methods and physics-based model fault detection methods against physics-based model fault detection methods. This compartmentalized evaluation impedes a comprehensive understanding of the strengths and limitations of each approach. To address this gap, we propose two novel physics-based models for the angle of attack (AoA) sensor fault detection: Manhattan Bias and Gain Tracking (MBGT) and Innovation Monitoring with Covariance Regularization (IMCR), both utilizing the Extended Kalman Filter (EKF). The proposed methods use a reduced order fixed-wing aircraft physics model developed using the first principles of physics and sensor fusion. We benchmarked these methods against machine learning-based approaches, including Long Short-Term Memory (LSTM) with residual analysis. The MBGT and IMCR are validated using flight data from the ATTAS research aircraft. The fault detection methods are evaluated under fault and fault-free conditions. Sensitivity analyses using a noisy sensor test dataset are also conducted. The results indicate that the MBGT and IMCR achieve near-zero false positive rates (FPR) under fault-free conditions. For ramp faults, the detection delays are 0.2 s for the IMCR and 0.18 s for the MBGT, demonstrating high responsiveness. In contrast, machine learning-based methods gave 0.4 s delay for ramp faults. Although physics-based methods are efficient and computationally lightweight, data-driven approaches, particularly LSTM, offer superior performance in noisy sensor environments and achieve lower FPR. The results show that a hybrid method is effective for fault detection.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"265 ","pages":"Article 120416"},"PeriodicalIF":5.6,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980138","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 : 2026-01-12DOI: 10.1016/j.measurement.2026.120429
Antonio Nocera , Lorenzo Luciani , Gianluca Ciattaglia , Michela Raimondi , Laura Burattini , Susanna Spinsante , Ennio Gambi , Rossana Galassi
Raman spectroscopy is a versatile analytical tool, yet it often struggles with low sensitivity, hardware noise, and environmental interference. To address these limitations, this study presents an automated, Artificial Intelligence (AI)-assisted methodology to convert noisy optical signals into robust digital measurements.
The process involves acquiring high-dimensional, noisy spectral data from analyte solutions. A grid search across various algorithms identifies the optimal pre-processing pipeline to minimize noise variance and ensure metrological repeatability. Instead of relying on raw sensor feeds, the method fits a Gaussian curve combined with a polynomial baseline to the data, extracting precise measurements from the peak of this mathematical model. Supported by AI, the method successfully separates multiple optical signals and their shifts originating from interactions among analytes, proving itself capable to compensate also for possible hardware misalignment and thermal drift. As such, it can be used to quantify the concentration of selected inorganic pollutants in a mixture of analytes.
The primary application addressed in this work is quantifying inorganic pollutants in water, to enable in situ analysis without continuous expert supervision. Tests on binary and ternary mixtures of inorganic pollutants in pure water demonstrated that the Mean Absolute Percentage Error (MAPE) for nitrate was consistently below 10% in the concentration range between 0 mg/L to more than 15 000 mg/L, dropping to under 5% for concentrations exceeding 1000 mg/L. For concentrations below 1000 mg/L, the Mean Absolute Error (MAE) values were 67 mg/L for nitrate, 1475 mg/L for sulfate, and 736 mg/L for nitrite, respectively.
{"title":"AI-assisted methodology for robust digital measurements by Raman spectroscopy: Quantification of inorganic pollutants in water","authors":"Antonio Nocera , Lorenzo Luciani , Gianluca Ciattaglia , Michela Raimondi , Laura Burattini , Susanna Spinsante , Ennio Gambi , Rossana Galassi","doi":"10.1016/j.measurement.2026.120429","DOIUrl":"10.1016/j.measurement.2026.120429","url":null,"abstract":"<div><div>Raman spectroscopy is a versatile analytical tool, yet it often struggles with low sensitivity, hardware noise, and environmental interference. To address these limitations, this study presents an automated, Artificial Intelligence (AI)-assisted methodology to convert noisy optical signals into robust digital measurements.</div><div>The process involves acquiring high-dimensional, noisy spectral data from analyte solutions. A grid search across various algorithms identifies the optimal pre-processing pipeline to minimize noise variance and ensure metrological repeatability. Instead of relying on raw sensor feeds, the method fits a Gaussian curve combined with a polynomial baseline to the data, extracting precise measurements from the peak of this mathematical model. Supported by AI, the method successfully separates multiple optical signals and their shifts originating from interactions among analytes, proving itself capable to compensate also for possible hardware misalignment and thermal drift. As such, it can be used to quantify the concentration of selected inorganic pollutants in a mixture of analytes.</div><div>The primary application addressed in this work is quantifying inorganic pollutants in water, to enable in situ analysis without continuous expert supervision. Tests on binary and ternary mixtures of inorganic pollutants in pure water demonstrated that the Mean Absolute Percentage Error (MAPE) for nitrate was consistently below 10% in the concentration range between 0 mg/L to more than 15<!--> <!-->000<!--> <!-->mg/L, dropping to under 5% for concentrations exceeding 1000<!--> <!-->mg/L. For concentrations below 1000<!--> <!-->mg/L, the Mean Absolute Error (MAE) values were 67<!--> <!-->mg/L for nitrate, 1475<!--> <!-->mg/L for sulfate, and 736<!--> <!-->mg/L for nitrite, respectively.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"265 ","pages":"Article 120429"},"PeriodicalIF":5.6,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980598","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 : 2026-01-12DOI: 10.1016/j.measurement.2026.120413
Shiqi Xiong , Chao Zhang , Zhiming Hu , Jiahang Liang , Jianjun Ding , Chao Sun
To overcome the limitations of single-modal vision in high-precision industrial robotic grasping and the high randomness, low search efficiency, and long planning time of traditional Rapidly Expanding Random Tree-Connect (RRT-Connect) algorithms, we propose an integrated approach combining multimodal visual detection with multi-directional artificial potential field-guided path planning. A lightweight Single Stage Detection (SSD) network quickly generates Two-Dimensional (2D) bounding boxes to constrain the search space for Point Pair Feature (PPF) pose estimation, while an improved PPF extracts geometric contours from point clouds for fast, accurate pose estimation. To resolve multiple solutions and pose ambiguities caused by rotationally symmetric objects and point cloud noise, a Generative Residual Convolutional Network (GR-ConvNet) generates optimal grasping poses, filtered against improved PPF outputs to yield precise and robust grasping data.For path planning, the Multi-Directional Artificial Potential Field-Guided RRT-Connect (MD-APF-Guided RRT-Connect) algorithm adds a third vertex at the midpoint between start and target points for multi-directional tree expansion, increasing connection probability. A virtual artificial potential field guides tree growth via a gradient-based composite field: the attraction field generates progressive pulling forces with path smoothing and kinematic constraints, while the repulsion field adaptively forms flexible obstacle avoidance regions, accelerating convergence and improving obstacle avoidance.Experiments show this method boosts grasp success by 10.30%, recognition speed by 12.50%, cuts positional and angular errors by 52.80% and 63.80%, shortens path length by 12.40%, reduces planning time by 30.58%, and lowers iterations by 19.72%, significantly improving grasping and path-planning efficiency and accuracy for autonomous industrial grasping.
{"title":"Research on multimodal pose estimation and grasping methods for complex workpieces","authors":"Shiqi Xiong , Chao Zhang , Zhiming Hu , Jiahang Liang , Jianjun Ding , Chao Sun","doi":"10.1016/j.measurement.2026.120413","DOIUrl":"10.1016/j.measurement.2026.120413","url":null,"abstract":"<div><div>To overcome the limitations of single-modal vision in high-precision industrial robotic grasping and the high randomness, low search efficiency, and long planning time of traditional Rapidly Expanding Random Tree-Connect (RRT-Connect) algorithms, we propose an integrated approach combining multimodal visual detection with multi-directional artificial potential field-guided path planning. A lightweight Single Stage Detection (SSD) network quickly generates Two-Dimensional (2D) bounding boxes to constrain the search space for Point Pair Feature (PPF) pose estimation, while an improved PPF extracts geometric contours from point clouds for fast, accurate pose estimation. To resolve multiple solutions and pose ambiguities caused by rotationally symmetric objects and point cloud noise, a Generative Residual Convolutional Network (GR-ConvNet) generates optimal grasping poses, filtered against improved PPF outputs to yield precise and robust grasping data.For path planning, the Multi-Directional Artificial Potential Field-Guided RRT-Connect (MD-APF-Guided RRT-Connect) algorithm adds a third vertex at the midpoint between start and target points for multi-directional tree expansion, increasing connection probability. A virtual artificial potential field guides tree growth via a gradient-based composite field: the attraction field generates progressive pulling forces with path smoothing and kinematic constraints, while the repulsion field adaptively forms flexible obstacle avoidance regions, accelerating convergence and improving obstacle avoidance.Experiments show this method boosts grasp success by 10.30%, recognition speed by 12.50%, cuts positional and angular errors by 52.80% and 63.80%, shortens path length by 12.40%, reduces planning time by 30.58%, and lowers iterations by 19.72%, significantly improving grasping and path-planning efficiency and accuracy for autonomous industrial grasping.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"265 ","pages":"Article 120413"},"PeriodicalIF":5.6,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980029","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 : 2026-01-12DOI: 10.1016/j.measurement.2026.120381
Congyue Li , Guobin Li , Pengfei Xing , Yijin Sui , Dexin Cui , Wenzhi He , Yu Liu , Hongpeng Zhang
The propulsion shafting is a core component of the ship power system, and its operational status directly affects navigational safety. Deep transfer learning has been widely applied in ship intelligent diagnostics. However, current diagnostic methods fail to fully leverage the correlation of output information across different iteration rounds and overlook the crucial impact of data representation quality in network generalization. To address this issue, this study proposes a dual-classifier adversarial learning diagnostic framework that dynamically senses variations in predicted label information. Specifically, this framework utilizes Hilbert transform-enhanced symmetrized dot patterns as input, aiming to extract rich domain-invariant and more discriminative semantic information. Subsequently, a two-stage pseudo-label purification module is designed to mitigate the interference of false pseudo-labels, thereby providing high-quality supervision for the model to learn from the target data. Meanwhile, a dual-classifier output uncertainty metric is constructed to guide the classification boundaries through low-density regions as much as possible. The proposed method was validated on a propulsion shafting test bench. The results demonstrate that the average diagnostic accuracy of the proposed method across six diagnostic tasks is 94.97%, outperforming other methods by 0.89% to 5.22%. Furthermore, the method achieves an average diagnostic accuracy exceeding 97% on two publicly available datasets, further confirming its excellent adaptability and generalizability.
{"title":"A semi-supervised learning method based on pseudo-label iterative purification for ship propulsion shafting fault diagnosis","authors":"Congyue Li , Guobin Li , Pengfei Xing , Yijin Sui , Dexin Cui , Wenzhi He , Yu Liu , Hongpeng Zhang","doi":"10.1016/j.measurement.2026.120381","DOIUrl":"10.1016/j.measurement.2026.120381","url":null,"abstract":"<div><div>The propulsion shafting is a core component of the ship power system, and its operational status directly affects navigational safety. Deep transfer learning has been widely applied in ship intelligent diagnostics. However, current diagnostic methods fail to fully leverage the correlation of output information across different iteration rounds and overlook the crucial impact of data representation quality in network generalization. To address this issue, this study proposes a dual-classifier adversarial learning diagnostic framework that dynamically senses variations in predicted label information. Specifically, this framework utilizes Hilbert transform-enhanced symmetrized dot patterns as input, aiming to extract rich domain-invariant and more discriminative semantic information. Subsequently, a two-stage pseudo-label purification module is designed to mitigate the interference of false pseudo-labels, thereby providing high-quality supervision for the model to learn from the target data. Meanwhile, a dual-classifier output uncertainty metric is constructed to guide the classification boundaries through low-density regions as much as possible. The proposed method was validated on a propulsion shafting test bench. The results demonstrate that the average diagnostic accuracy of the proposed method across six diagnostic tasks is 94.97%, outperforming other methods by 0.89% to 5.22%. Furthermore, the method achieves an average diagnostic accuracy exceeding 97% on two publicly available datasets, further confirming its excellent adaptability and generalizability.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"265 ","pages":"Article 120381"},"PeriodicalIF":5.6,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980031","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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