Measurement最新文献

{"title":"In situ control of atom spin in SERF comagnetometer based on dynamic polarization feedback","authors":"Saixin Zhou ,&nbsp;Xiaofei Huang ,&nbsp;Yang Rui ,&nbsp;Jie Zheng ,&nbsp;Wei Quan ,&nbsp;Kai Wei","doi":"10.1016/j.measurement.2025.119188","DOIUrl":"10.1016/j.measurement.2025.119188","url":null,"abstract":"<div><div>Quantum precision measurements offer superior sensitivity; however, their practical deployment remains constrained by environmental perturbations and quantum decoherence. This study proposes a dynamic polarization feedback technique based on pulsed optical modulation, which enables in-situ stabilization control of electron spin polarization without compromising the self-compensation regime of the spin-exchange relaxation-free (SERF) comagnetometer system. We develop a theoretical model to analyze the electron spin longitudinal polarization response and its dynamic optical absorption measurement in a K-Rb-²¹Ne comagnetometer under modulation scheme, validating high-precision electron spin polarization measurements. By implementing the closed-loop control system, we suppress low-frequency noise by approximately 6.8 dB, achieving a sensitivity of 3.3 × 10⁻⁶ deg/s/Hz^1/2 @1Hz. Long-term system drift is effectively mitigated, and the Allan deviation curve indicates a 12 dB improvement in the bias instability of system, reaching 8.2 × 10⁻³ deg/h @27 s. This work significantly enhances SERF comagnetometer robustness and extends its utility for long-duration measurements of anomalous spin-dependent interactions. Furthermore, it offers broad application potential in quantum spin-based sensors for geophysical exploration, magnetoencephalography and magnetocardiography.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119188"},"PeriodicalIF":5.6,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145247843","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}

{"title":"A comparative study of battery state of charge estimation with equivalent circuit model and empirical model","authors":"Zichuan Ni,&nbsp;Chao Jia","doi":"10.1016/j.measurement.2025.119162","DOIUrl":"10.1016/j.measurement.2025.119162","url":null,"abstract":"<div><div>The equivalent circuit model and empirical model are commonly used for battery prognostics for electric vehicles in industry. This paper gives a comprehensive comparison study from system modeling to parameter estimation and state of charge (SOC) estimation between the two models. For the equivalent circuit model, an accurate parameter identification method is applied by deducing least square form with the pulse discharging data, and the SOC is estimated by designing a reduced-order observer. For the empirical model, the parameters are determined directly from dynamic testing data, and then a novel modified Luenberger observer is designed for SOC estimation for the nonlinear system, where the convergence is validated by designing a Lyapunov function. Finally, a comprehensive comparison analysis is demonstrated in model structure, modeling accuracy, SOC estimation accuracy, feasibility, initial bias situations and hyperparameters, which provide practical guide for industrial applications.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119162"},"PeriodicalIF":5.6,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222275","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}

{"title":"A magnetic field reinforced excitation structure for enhanced motion-induced eddy current defect detection","authors":"Bingkun Wei,&nbsp;Lisha Peng,&nbsp;Yangbo Liu,&nbsp;Jinghua Zhang,&nbsp;Shuzhi Wen,&nbsp;Shisong Li,&nbsp;Songling Huang","doi":"10.1016/j.measurement.2025.119186","DOIUrl":"10.1016/j.measurement.2025.119186","url":null,"abstract":"<div><div>Copper and aluminum foils, serving as current collectors in lithium-ion batteries, are manufactured by continuously rolling thick plates to the desired thin-foil thickness. Once defects occur during the rolling of thick plates, they inevitably propagate into the foils, thereby compromising production quality and further deteriorating the electrochemical performance and safety of batteries. Conventional detection methods based on a single permanent magnet or DC coil often generate weak responses to small defects, making them unsuitable for high-speed manufacturing. To overcome this limitation, this study proposes a magnetic-field-reinforced excitation structure and establishes a theoretical model to analyze the effects of magnet spacing and lift-off distance on magnetic field characteristics. Finite element simulations are employed to investigate their influence on motion-induced eddy current (MIEC) signals. Experimental results show that the proposed structure enhances defect signal amplitudes by 59.9–92.7 % compared with single magnet configurations, with particularly significant improvements for small defect sizes and short lift-off distances. This work provides an effective method for high-speed defect detection and quality control of copper and aluminum foils in battery manufacturing.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119186"},"PeriodicalIF":5.6,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269081","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}

{"title":"Adaptive sliding mode modular permanent magnet synchronous machine efficiency optimal cooperative control based on improved ring coupling","authors":"Yingying Xu ,&nbsp;Xianzheng Su ,&nbsp;Yanjun Ge ,&nbsp;Mingxia Xu ,&nbsp;Qingguang Chi","doi":"10.1016/j.measurement.2025.119187","DOIUrl":"10.1016/j.measurement.2025.119187","url":null,"abstract":"<div><div>Large-scale mechanical equipment frequently use modular motor technology to reduce power redundancy. In multi-module motors, improved efficiency and precise synchronous control are essential for energy conservation and system stability. In this paper, we propose an adaptive sliding mode modular permanent magnet synchronous machine (MPMSM) efficiency optimal cooperative control based on improved ring coupling. An efficiency optimal control (EOC) is established to determine the efficiency optimization instruction. A multi-module adaptive sliding mode control (ASMC) cooperative control based on improved ring coupling is developed to achieve precise synchronous control. An improved ring coupling control aims to establish the ring coupling relationship between modules while introducing error compensation to mitigate synchronization errors resulting from module coupling. An ASMC with an adaptive reaching law is designed to address the issues of friction, parameter variations, and load disturbances in single-module operation, therefore significantly improving speed tracking accuracy by real-time adjustments of the reaching law gain. Subsequently, the integration of EOC with cooperative control can autonomously regulate the number of operational modules to improve efficiency while significantly reducing synchronization error. Simulation and experimental findings indicate that the proposed technique markedly improves operational efficiency, realizing a 4.28 % improvement under light load conditions. In comparison to other approaches under different conditions, the control system demonstrates superiority in robustness, synchronization error suppression, and dynamic convergence speed. This research provides a novel control technique for achieving high efficiency and precision in the synchronous control of MPMSM.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119187"},"PeriodicalIF":5.6,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269073","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}

{"title":"Hybrid CNN-LSTM model for concentration retrieval in active fiber loop ring-down spectroscopy under EDFA gain saturation","authors":"Yawen Li ,&nbsp;Pengpeng Wang ,&nbsp;Yujie Duan ,&nbsp;Zhanshang Su ,&nbsp;Tianxiang Zhao ,&nbsp;Xiangxian Li ,&nbsp;Cunguang Zhu","doi":"10.1016/j.measurement.2025.119193","DOIUrl":"10.1016/j.measurement.2025.119193","url":null,"abstract":"<div><div>In active fiber loop ring-down spectroscopy (FLRDS) systems, gain saturation in the erbium-doped fiber amplifier (EDFA) causes inter-pulse gain fluctuations, resulting in significant deviations from ideal exponential decay. These deviations compromise measurement accuracy because traditional methods depend on precise extraction of the ring-down time (<em>τ</em>). To address this issue, we propose a hybrid deep learning framework that combines a convolutional neural network (CNN) with a long short-term memory (LSTM) network for FLRDS-based gas sensing. This architecture captures both local temporal features and long-range dependencies, enabling nonlinear compensation that maps distorted ring-down signals directly to gas concentrations—bypassing the error-prone step of τ estimation. Experimental results show that the CNN-LSTM model substantially improves concentration retrieval accuracy, outperforming not only standalone CNN and LSTM models, but also other common machine and deep learning methods in key metrics such as RMSE, MAE, MAPE, and R². Over a concentration range of 40–1600 ppm, the model effectively mitigates EDFA gain saturation effects, achieving relative errors below 2.5 % and offering improved robustness for practical gas sensing. An uncertainty budget analysis further confirms that the proposed method yields lower combined standard uncertainty and narrower confidence intervals than exponential fitting, underscoring its reliability for high-precision gas detection.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119193"},"PeriodicalIF":5.6,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145222265","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}

{"title":"Attention-based multi-level feature fusion for voice disorder diagnosis","authors":"Lipeng Shen ,&nbsp;Yifan Xiong ,&nbsp;Dongyue Guo ,&nbsp;Wei Mo ,&nbsp;Lingyu Yu ,&nbsp;Hui Yang ,&nbsp;Yi Lin","doi":"10.1016/j.measurement.2025.119168","DOIUrl":"10.1016/j.measurement.2025.119168","url":null,"abstract":"<div><div>Voice disorders negatively impact the quality of daily life in various ways. However, accurately recognizing the category of pathological features from raw audio remains a considerable challenge due to the limited dataset. A promising method to handle this issue is extracting multi-level pathological information from speech in a comprehensive manner by fusing features in the latent space. In this paper, a novel framework is designed to explore the way of high-quality feature fusion for effective and generalized detection performance. Specifically, the proposed model follows a two-stage training paradigm: (1) ECAPA-TDNN and Wav2vec 2.0 which have shown remarkable effectiveness in various domains are employed to learn the universal pathological information from raw audio; (2) An attentive fusion module is dedicatedly designed to establish the interaction between pathological features projected by ECAPA-TDNN and Wav2vec 2.0 respectively and guide the multi-layer fusion, the entire model is jointly fine-tuned from pre-trained features by the automatic voice pathology detection task. Finally, comprehensive experiments demonstrate that the proposed framework outperforms the competitive baselines, achieving the accuracy of 90.51% and 87.68% on the FEMH and SVD datasets, respectively. Furthermore, the proposed framework can achieve the comparable performance of selective baselines with only 70% of the training dataset.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119168"},"PeriodicalIF":5.6,"publicationDate":"2025-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145268640","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}

{"title":"High impedance fault detection for resonant grounding distribution systems based on transient component full-period differentiated measurement","authors":"Xiaowei Wang ,&nbsp;Yizhe Luo ,&nbsp;Jie Gao ,&nbsp;Xiangxiang Wei ,&nbsp;Fan Zhang ,&nbsp;Peng Wang","doi":"10.1016/j.measurement.2025.119172","DOIUrl":"10.1016/j.measurement.2025.119172","url":null,"abstract":"<div><div>High impedance fault (HIF) signals in resonant grounding systems are weak and easily confused with conventional disturbances, which makes detection challenging. Existing single-criterion detection methods often lack reliability, while multi-criterion approaches offer limited improvement due to insufficient complementarity among the criteria. To address this issue, a detection method based on full-period segmented analysis of transient components is proposed. First, the differences in polarity, energy, and similarity of transient components between faulty and healthy feeders under single-phase HIFs are analyzed. Next, the transient voltage and current components within three cycles after the fault are extracted and divided into three segments. In Segment ①, Criterion 1 is established based on the opposite polarity of the transient current projection coefficients onto the transient voltage between the faulty and healthy feeders. In Segment ②, Criterion 2 is derived from the maximum deviation in normalized transient average energy. In Segment ③, Criterion 3 utilizes the minimum similarity between the normalized transient component of the faulty feeder and those of the healthy feeders. Meanwhile, to balance speed and reliability, correction weights for the criteria are assigned based on energy ratios across different segments, and corresponding logic thresholds are applied to achieve accurate HIF detection. Simulation and field test results verify that the proposed method is resistant to disturbances, unaffected by transition resistance, fault location, and initial phase, and remains effective under arcing conditions and noise interference.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119172"},"PeriodicalIF":5.6,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221651","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}

{"title":"Phase error correction algorithm for discontinuous reflectivity regions","authors":"Rui Gao,&nbsp;Yuchong Chen,&nbsp;Feipeng Da,&nbsp;Shaoyan Gai","doi":"10.1016/j.measurement.2025.119023","DOIUrl":"10.1016/j.measurement.2025.119023","url":null,"abstract":"<div><div>Current research indicates that camera defocus can adversely affect phase accuracy in regions with abrupt reflectivity changes, particularly for objects with complex textures. To address this challenge, this paper constructs an error model, revealing a trigonometric relationship between phase errors and the tangent angle of the texture. By investigating the variation patterns of this error model, a one-dimensional phase-weighted average and parameter fitting model is proposed to correct phase errors. The proposed method requires only the projection of unidirectional fringe patterns for phase error correction, offering faster processing and higher stability compared to methods relying on bidirectional fringe patterns. The experimental results demonstrate that the algorithm proposed in this paper can effectively reduce systematic errors and improve the accuracy of the final point clouds reconstruction.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119023"},"PeriodicalIF":5.6,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269678","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}

{"title":"Highly efficient Coordinate Measuring Machine error compensation via Greedy Randomized Kaczmarz algorithm and nongeometric error identification neural network","authors":"Jian Liang ,&nbsp;Zefeng Sun ,&nbsp;Jiehu Kang ,&nbsp;Shuyang Wang ,&nbsp;Zongyang Zhao ,&nbsp;Shangyong Li ,&nbsp;Shanzhai Feng ,&nbsp;Mingji Zhen ,&nbsp;Bin Wu","doi":"10.1016/j.measurement.2025.119091","DOIUrl":"10.1016/j.measurement.2025.119091","url":null,"abstract":"<div><div>Coordinate Measuring Machines (CMMs) are essential for high-precision measurements in modern manufacturing. However, their accuracy is often compromised by geometric and nongeometric errors. This paper presents a comprehensive error compensation method that integrates model-based and data-driven approaches. Geometric error compensation is achieved through the Product of Exponentials (POE) formula for modeling and the Greedy Randomized Kaczmarz (GRK) algorithm for efficient parameter identification. For nongeometric errors, a data-driven approach is employed using the High-Precision and Lightweight Nongeometric Error Identification Neural Network (NEINN). It introduces a novel network architecture, which incorporates compensation information from neighboring points to enhance robustness and prediction accuracy while mitigating overfitting. Experimental tests were conducted on a CMM with a nominal accuracy of 1.5 <span><math><mrow><mi>μ</mi><mi>m</mi><mo>+</mo><mi>L</mi><mspace></mspace><mrow><mo>[</mo><mi>mm</mi><mo>]</mo></mrow><mo>/</mo><mn>400</mn><mspace></mspace><mi>μ</mi><mi>m</mi></mrow></math></span>, using a laser tracking interferometer as the high-precision calibration device. In the geometric error compensation experiment, a total of 738 unknown parameters were identified, and 567 calibration points were measured. The parameter identification process took 4.1 s, resulting in a 56% improvement in efficiency compared to the traditional Levenberg–Marquardt algorithm. For nongeometric error compensation, a dataset of 4,000 samples was collected for training and testing. The designed NEINN network outperforms existing methods in key evaluation metrics, including Root Mean Squared Error and Mean Absolute Error, significantly enhancing overall error compensation performance. Validation tests conducted using ISO 10360 standards show that the CMM compensated with our method achieves high measurement accuracy, with a length measurement error of 0.5 <span><math><mrow><mi>μ</mi><mi>m</mi><mo>+</mo><mi>L</mi><mspace></mspace><mrow><mo>[</mo><mi>mm</mi><mo>]</mo></mrow><mo>/</mo><mn>400</mn><mspace></mspace><mi>μ</mi><mi>m</mi></mrow></math></span>, and a detection error <span><math><mrow><mn>0</mn><mo>.</mo><mn>25</mn><mspace></mspace><mi>μ</mi><mi>m</mi></mrow></math></span>. Furthermore, tests across various CMMs and environmental conditions confirm the effectiveness and practical applicability of the proposed approach. The method significantly enhances CMM performance, improving both measurement precision and the efficiency of the error compensation process, thus providing a scalable solution for industrial applications. However, the proposed method assumes a rigid-body model for the CMM, which may limit its applicability in dynamic operational scenarios. Future work will aim to address this limitation, further enhancing the method’s robustness and expanding its range of practical applications.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119091"},"PeriodicalIF":5.6,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221656","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}

{"title":"Process-dynamics-guided latent predictability embedding supervised deep networks for soft sensing in industrial processes","authors":"Zhengxuan Zhang ,&nbsp;Xu Yang ,&nbsp;Jian Huang ,&nbsp;Yuri A.W. Shardt ,&nbsp;Jingjing Gao ,&nbsp;Kaixiang Peng","doi":"10.1016/j.measurement.2025.119139","DOIUrl":"10.1016/j.measurement.2025.119139","url":null,"abstract":"<div><div>Soft sensors for complex industrial processes have become a challenging task due to dynamic self-correlation caused by the feedback loop and inertia effects. Although the dynamic latent variable models offer an interpretable solution, the linear latent variables fail to capture the behavioral characteristics of strongly nonlinear industrial processes. Thus, this article proposes a new deep stacked autoencoder with latent predictability embedding for soft sensing, which is called the process-dynamics-guided latent predictability embedding supervised deep network (PDLPSDN). To capture the autocorrelation in the process data, a regularization term based on the point prediction is embedded into the decoding loss. Subsequently, information theory is used to link the contribution from past time steps to the present, which is used to guide the structure of the latent dynamics. Finally, the parameter-guided regularization terms assist in learning the temporal dependencies in the process data and are then trained in an alternating manner. The proposed PDLPSDN decreases the root mean squared error by 16.8% for the debutanizer column and 25.7% for the sulfur-recovery unit, demonstrating the reliable and superior performance of the proposed PDLPSDN-based soft sensing.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119139"},"PeriodicalIF":5.6,"publicationDate":"2025-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145221654","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}