Measurement最新文献

{"title":"Study on deformation monitoring and pre-failure information identification of coal samples based on OFDR-AE","authors":"Dingding Zhang ,&nbsp;Long Yang ,&nbsp;Mengqing Qin ,&nbsp;Jing Chai","doi":"10.1016/j.measurement.2025.117820","DOIUrl":"10.1016/j.measurement.2025.117820","url":null,"abstract":"<div><div>In order to obtain internal strain evolution during crack propagation and damage in coal samples, this paper proposes a combined monitoring approach using Optical Frequency Domain Reflectometry (OFDR) and Acoustic Emission (AE) technologies. Five optical fiber sensors and AE probes were installed on a cubic coal sample (100 * 100 * 100 mm), and uniaxial compression tests were conducted to examine the strain evolution under both uniaxially and the staged loading paths. The results indicate that OFDR accurately measures internal strain and captures strain evolution across various loading stages, including compaction, elastic, yield, and post-peak failure. A sharp increase in the non-uniform deformation index (<em>Sw</em>) during the yield stage and a “fluctuation-stabilization-decline” trend in the AE <em>b</em>-value serve as precursors to coal body failure. This research further introduces the <em>Sw</em>/<em>b</em> failure criterion, providing an effective basis for predicting the failure mode of coal bodies. By combining OFDR and AE techniques, this research addresses the spatial and temporal resolution limitations of traditional monitoring methods. The proposed <em>Sw</em>/<em>b</em> failure criterion offers valuable insights for monitoring coal body deformation and damage.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117820"},"PeriodicalIF":5.2,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942411","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":"An auxetic based geometric tuning approach in microwave sensors for enhanced sensitivity in strain measurements","authors":"A.P. Praveen ,&nbsp;Sreedevi K. Menon ,&nbsp;Jeetu S. Babu ,&nbsp;Massimo Donelli ,&nbsp;M.P. Hariprasad","doi":"10.1016/j.measurement.2025.117826","DOIUrl":"10.1016/j.measurement.2025.117826","url":null,"abstract":"<div><div>Reducing sensor size without compromising the antenna’s characteristics is quite a challenge in radio frequency (RF) sensors for strain measurement, due to the dependence of sensor size on wavelength. This paper explores the possibility of utilising an auxetic structure in a patch antenna to enhance the strain sensitivity at lower strains. A new design concept of auxetic-microstrip patch antenna (AMSPA) sensor is introduced for the first time in strain measurements. The radiating element of a microstrip patch antenna is modified with a hexagonal re-entrant auxetic (negative Poisson’s ratio) cluster without altering the resonant frequency. The structural and electromagnetic co-simulations are performed to investigate the strain sensitivity under tensile loads. The simulation and experiment results indicate that the AMSPA sensor is suitable for detecting lower strains without deteriorating the antenna characteristics. The results show that the proposed sensor has a higher sensitivity of −4.157 ± 0.5 kHz /µε at lower strains with an enhanced gain (+12.2 %) in comparison with conventional microstrip antenna. The auxetic architecture enable sensor miniaturization by introducing additional surface current paths without degrading the antenna performance.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117826"},"PeriodicalIF":5.2,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942287","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":"Research on seawater temperature and pressure sensor based on microfiber resonator","authors":"Peng Gao ,&nbsp;Jing Gao ,&nbsp;Bo Han ,&nbsp;Bo-wen Zheng ,&nbsp;Feng Xia","doi":"10.1016/j.measurement.2025.117776","DOIUrl":"10.1016/j.measurement.2025.117776","url":null,"abstract":"<div><div>Aiming at the requirement of simultaneous measurement of seawater temperature and pressure, this paper presents a two-parameter sensing scheme of temperature and pressure using elliptical microfiber knot resonator (MKR). Its output spectrum consists of interference spectrum formed by modal interference within the microfiber and variant Vernier spectrum formed by resonance of different modes in the resonant ring region. The interference spectrum and variant Vernier spectrum were separated through Fast Fourier Transform (FFT) filtering. After analysis, pressure sensitivity of interference dip and variant Vernier envelope reached −46.68 pm/N and −63.46 pm/N, respectively, in the pressure range of 20 N ∼ 220 N. The maximal pressure sensitivity obtained by theoretical conversion is approximately −8.44 nm/MPa, and the pressure resolution is 2.37 × 10^-3 MPa, which corresponds to the water depth of 0.24 m. For interference dip and variant Vernier envelope, temperature sensitivities are −4.79 nm/℃ and −3.84 nm/℃ in 22℃∼38℃, respectively. This sensing structure shows good time stability, repeatability and hysteresis. The simultaneous demodulation of temperature and pressure is achieved by means of sensitivity matrix inversion. The sensing structure, which has a compact size, strong robustness, and high sensitivity, has a good application prospect for seawater temperature and pressure two-parameter sensing.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117776"},"PeriodicalIF":5.2,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942288","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":"Prediction method for instrument transformer measurement error: Adaptive decomposition and hybrid deep learning models","authors":"Zhenhua Li ,&nbsp;Jiuxi Cui ,&nbsp;Heping Lu ,&nbsp;Feng Zhou ,&nbsp;Yinglong Diao ,&nbsp;Zhenxing Li","doi":"10.1016/j.measurement.2025.117592","DOIUrl":"10.1016/j.measurement.2025.117592","url":null,"abstract":"<div><div>The measurement accuracy of current transformers is crucial for power system protection and trade fairness. The high penetration of renewable energy into the power grid has affected the transient performance of power systems, posing significant challenges for accurate current transformer measurement. To address this issue, this paper proposes a prediction model for transformer measurement accuracy based on an adaptive dual-modal decomposition strategy and a hybrid deep learning architecture. The framework integrates an enhanced Adaptive Time-Varying Filter (A-TVF), an enhanced Adaptive Variational Mode Decomposition (A-VMD), the Residual Error Index (REI), and the Maximum Information Coefficient (MIC). First, A-TVF preprocesses the collected data by setting REI as the optimization objective to adaptively adjust filter construction parameters, including the B-spline order, bandwidth threshold, and decomposition number, and decomposes the collected ratio error sequence to reduce the non-stationarity of the original sequence. Subsequently, indices such as PE and Kurt are used to screen the decomposed sub-sequences and reconstruct the complex components. Then, A-VMD is applied to further decompose the complex components, minimizing MIC by adaptively determining the decomposition number, penalty factor, convergence accuracy, and fidelity parameters. Afterward, the complexity of the subcomponents obtained from the secondary decomposition is calculated, and the entire sequence is reconstructed. Finally, a hierarchical prediction model integrating Temporal Convolutional Networks (TCN), Bidirectional Gated Recurrent Units (BiGRU), and a Multi-Head Attention mechanism (MHA) is employed to predict the reconstructed components and generate the final results. Experimental results demonstrate that the proposed adaptive dual-modal decomposition method significantly improves prediction performance: compared with non-decomposition models, RMSE, MAE, and SMAPE were reduced by an average of 50.12%, 46.09%, and 37.70% in global decomposition scenarios, and by 25.92%, 23.69%, and 19.96% in rolling decomposition scenarios, respectively. These results validate the effectiveness of the proposed method in reducing data complexity and improving the accuracy and stability of Ratio Error predictions.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117592"},"PeriodicalIF":5.2,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144070222","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":"Application of machine learning models for predicting zinc oxide nanoparticle size","authors":"Surafel Alayou,&nbsp;Mekdes Mengesha,&nbsp;Getachew Tizazu","doi":"10.1016/j.measurement.2025.117785","DOIUrl":"10.1016/j.measurement.2025.117785","url":null,"abstract":"<div><div>Accurate characterization of nanostructures is essential for optimizing their properties for various applications, yet conventional methods such as electron microscopy are costly and time-consuming. This study explores the potential of machine learning (ML) in predicting the size of zinc oxide (ZnO) nanoparticles using synthesis conditions and band gap. A dataset of 90 samples, comprising nine synthesis parameters, was compiled from published literature. These samples were divided into training (75 %) and testing (25 %) sets, and four ML models—Catboost (CB), Gradient Boosting (GB), Extreme Gradient Boosting (XGB), and a Stacking Ensemble—were implemented, with hyperparameter tuning performed using Randomized Search CV. Among these models, the Stacking Ensemble approach achieved the highest accuracy, with an R² value of 0.9377 and a mean absolute error (MAE) of 3.08 nm. Feature importance analysis identified the band gap as the most significant predictor of nanoparticle size, followed by calcination temperature, reaction time, precursor concentration, and reaction temperature. To further validate the model, an additional set of 25 unseen experimental datasets from previous studies was used, where the model closely predicted 17 instances (68 %). Additionally, ZnO nanoparticles were synthesized, and their size was estimated at 53.07 nm by the ML model, closely aligning with the scanning electron microscopy (SEM)-measured size of 58.9 nm. These findings underscore the potential of ML as a cost-effective alternative to traditional size characterization techniques. To enhance practical application, a user-friendly graphical interface (GUI) was developed, providing a scalable solution for nanoparticle size estimation while reducing reliance on experimental characterization and accelerating materials research.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117785"},"PeriodicalIF":5.2,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143934701","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":"Static EIS multi-frequency feature points combined with WOA-BP neural network for Li-ion battery SOH estimation","authors":"Zitong Gao ,&nbsp;Yuhong Jin ,&nbsp;Yuan Zhang ,&nbsp;Ziheng Zhang ,&nbsp;Siquan Li ,&nbsp;Jingbing Liu ,&nbsp;Hao Wang","doi":"10.1016/j.measurement.2025.117780","DOIUrl":"10.1016/j.measurement.2025.117780","url":null,"abstract":"<div><div>Electrochemical impedance spectroscopy (EIS) has been established as an essential and non-destructive technique for estimating the state of health (SOH) of lithium-ion batteries (LIBs). However, the extraction of effective and straightforward health indicators (HIs) from either original or derived EIS data is critical for practical applications in SOH estimation. In this study, we explore static EIS multi-frequency feature points as HI based on Nyquist plots at a various SOH for commercially available batteries. Subsequently, the extracted HIs are fed into a whale optimization algorithm back propagation (WOA-BP) neural network to achieve the accurate battery SOH estimation. The developed model is validated using four battery samples cycled at the same condition, which can achieve a low root-mean-square error range from 0.23 % to 0.43 %. Notably, even when employing the untrained data with our model, it can achieve a commendable root-mean-square error during practical validation. Moreover, our WOA-BP model exhibits superior accuracy compared to other mainstream algorithms and shows the significant application potential even without historic EIS data. The results indicate that selecting characteristic frequency points can greatly reduce testing time compared to utilizing full impedance spectroscopy as HI, presenting an effective strategy for battery SOH estimation in real-world applications.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117780"},"PeriodicalIF":5.2,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143931478","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":"Research on image processing-based oil particle contaminant detection methods and devices","authors":"Chenyong Wang ,&nbsp;Xurui Zhang ,&nbsp;Xinran Wang,&nbsp;Chenzhao Bai,&nbsp;Hongpeng Zhang","doi":"10.1016/j.measurement.2025.117718","DOIUrl":"10.1016/j.measurement.2025.117718","url":null,"abstract":"<div><div>With the continuous advancement of automation, regular condition monitoring and fault diagnosis are necessary to ensure the long-term operational stability of mechanical equipment. Lubricating oil, often referred to as the “lifeblood” of mechanical equipment, serves various functions such as energy transfer, anti-wear, system lubrication, corrosion prevention, rust prevention, and cooling. Therefore, detecting contaminants in the lubricating oil of mechanical equipment, especially particulate contaminants, is a prerequisite for ensuring the normal operation of the equipment. This paper designs and constructs an oil particle contaminant target detection algorithm based on Faster RCNN-Contrastive Language-Image Pretraining (CLIP), which combines a deep learning model with traditional image processing for extracting information about oil particle contaminants. This algorithm can extract information on oil particle pollutants’ location, type, quantity, size, and shape. On a smaller dataset, the MAP score of Faster RCNN-CLIP is 68.43%, the F1 score is 62.35%, while the corresponding metrics of Faster RCNN are 24.57% and 20.23%, respectively, and YOLOv5 are 40.79% and 31.22%. These results indicate that Faster RCNN-CLIP is a model more suitable for oil particle contaminant detection, especially under limited data <span><span>resources.An</span><svg><path></path></svg></span> oil particle contaminant detection device was also developed to break free from the laboratory constraints. This device integrates the oil above particle contaminant extraction model and mainly consists of an oil delivery unit, collection unit, control unit, display unit, and backend. The device is portable and efficient in detection and has IoT capabilities, enabling real-time remote detection of oil particle contaminants. This new method brings a more flexible and efficient solution to real-time fault diagnosis for oil condition monitoring.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117718"},"PeriodicalIF":5.2,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143942305","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":"Urban traffic monitoring through Distributed Acoustic Sensing: Trial analysis of a potent monitoring tool","authors":"Izhan Fakhruzi,&nbsp;Manuel Titos,&nbsp;Carmen Benítez,&nbsp;Luz García","doi":"10.1016/j.measurement.2025.117668","DOIUrl":"10.1016/j.measurement.2025.117668","url":null,"abstract":"<div><div>Distributed Acoustic Sensing (DAS) is a state-of-the-art sensing technology that transforms pre-existing fiber optic cables in cities into high-density arrays of sensors. It thus provides massive sensing capacity with high spatial and temporal resolution, becoming a potential attractive tool for urban traffic monitoring. This work presents a real uncontrolled urban-traffic monitoring experiment deployed in the city of Granada (Spain). The first analysis of the experiment reveals several categories of mobile events described in detail, proposing knowledge-based feature vectors to understand their characteristics and underlying discriminative information. A comprehensive exploratory data analysis is complemented with several state-of-the-art clustering techniques to address design foundational questions for data analysis like optimal frequency band, data separability, discriminative features selection, and identification of relevant spatial points. As a result, this study provides a working methodology for DAS technology pointing at challenges and future work paths using this potent urban monitoring tool.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117668"},"PeriodicalIF":5.2,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143947090","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":"HBIM symmetry parametrization using TLS and UAV LiDAR measurements","authors":"Pawel S. Dabrowski ,&nbsp;Marek H. Zienkiewicz ,&nbsp;Paweł Tysiąc ,&nbsp;Paweł Burdziakowski ,&nbsp;Jakub Szulwic ,&nbsp;Jūratė Sužiedelytė-Visockienė ,&nbsp;Eimuntas Paršeliūnas ,&nbsp;Romuald Obuchovski ,&nbsp;Rokas Bražiūnas ,&nbsp;Rafał Ossowski","doi":"10.1016/j.measurement.2025.117750","DOIUrl":"10.1016/j.measurement.2025.117750","url":null,"abstract":"<div><div>The paper describes a new approach to the assessment of symmetry in HBIM datasets on the example of the Tower of Gediminas in Vilnius (Lithuania). Symmetry is a principal component of the design and construction of ancient, medieval, Renaissance, and other epochs. The unified methodology involves the processing of LiDAR point clouds and applies to objects with a regular polygon cross-section. Proposed HBIM parameters introduce a uniform description of features of symmetry of historical buildings, which provides new insights into the original design of spatial relationships between the buildings’ architectural elements useful for professional conservators and renovators. The validation of the new approach involved well-established TLS and, taking into account the current development of drone technologies, ALS measurements. The obtained converging and similar estimates showed the applicability of both LiDAR technologies. Hence, the study proposes the introduction of the new symmetry parameters into the HBIM products and software.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117750"},"PeriodicalIF":5.2,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143934696","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":"Optimizing portable gas sensor system for hepatocellular carcinoma detection via volatile organic compounds analysis","authors":"Charusluk Viphavakit ,&nbsp;Thanikan Sukaram ,&nbsp;Soe Thiha Maung ,&nbsp;Rungsun Rerknimitr ,&nbsp;Roongruedee Chaiteerakij","doi":"10.1016/j.measurement.2025.117802","DOIUrl":"10.1016/j.measurement.2025.117802","url":null,"abstract":"<div><div>The analysis of volatile organic compounds (VOCs) in exhaled breath is a promising method for the early detection of hepatocellular carcinoma (HCC). However, the current technology suffers from a lack of portability, rendering it unsuitable for field-based screening applications. This study designed a compact sensor system operated in real-time, as detecting the 5 HCC biomarkers simultaneously was vital for accurate diagnosis due to cross-biomarkers for various diseases. The results showed that the optimized gas sensor system included injection volume of 3.0 mL, heating the Tedlar bag to 80 °C, and applying a 1-minute nitrogen purge, which achieved a rapid response time. The proposed gas sensor system has demonstrated excellent performance, with a concentration range of 10–1,000 ppb and strong linearity (R² &gt; 0.85). This portable analyzer is easy to use, delivers rapid results, and is suitable for point-of-care testing. Further studies are warranted to validate its performance with human breath samples before its implementation in clinical practice.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117802"},"PeriodicalIF":5.2,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143931539","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}