Measurement最新文献

{"title":"Zero/few-shot fault diagnosis of rotary mechanism in rotational inertial navigation system based on digital twin and transfer learning","authors":"Hui Li ,&nbsp;Gongliu Yang ,&nbsp;Ting Wang ,&nbsp;Jiao Zhou ,&nbsp;Yongqiang Tu ,&nbsp;Qingzhong Cai","doi":"10.1016/j.measurement.2025.119253","DOIUrl":"10.1016/j.measurement.2025.119253","url":null,"abstract":"<div><div>With the increasing demand for long-endurance, high-precision inertial navigation systems, rotational inertial navigation system (RINS) have become a research focus. However, the integration of rotary machinery introduces new challenges, including increased susceptibility to component failures, difficulties in collecting sufficient fault samples particularly for early stage faults and high costs and risks associated with fault injection testing. To address these challenges, this paper proposed a zero-shot fault diagnosis method for RINS based on digital-twin-assisted fault sample generation. By constructing a high-fidelity digital twin model, synthetic fault data are generated to compensate for the scarcity of actual fault samples. Furthermore, by integrating few-shot transfer learning with a small amount of real fault data, the diagnostic performance is further enhanced. Experimental results show that the proposed method achieves a fault diagnosis accuracy of 83.92%<!--> <!-->with binary classification accuracy reaching 96.86%Ẇhen few-shot transfer learning is applied, the classification accuracy exceeds 99%<!--> <!-->demonstrating the method’s effectiveness in overcoming the key challenges of RINS fault diagnosis.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119253"},"PeriodicalIF":5.6,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145270177","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":"On the applicability of speckle pattern imaging combined with AI for raw milk classification","authors":"Cristina Nuzzi ,&nbsp;Simone Pasinetti ,&nbsp;Irene Bassi ,&nbsp;Valentina Bello","doi":"10.1016/j.measurement.2025.119246","DOIUrl":"10.1016/j.measurement.2025.119246","url":null,"abstract":"<div><div>This work demonstrates the high potential of an innovative technique for raw milk classification based on the integration of speckle pattern imaging and artificial intelligence. By exciting speckle patterns with a semiconductor laser and collecting experimental images with a CMOS camera, a total of 20 samples of raw cow milk with similar nutritional contents were tested during 4 Campaigns. Data analysis was conducted leveraging one common feature-based machine learning model and one state-of-the-art image-based deep learning model for speckle patterns. This study aims to provide in-depth insights to the community on how this measurement technique can be applied to raw cow milk samples and how the prediction models tested perform due to the similarity of the nutritional components of the samples. The machine learning model was trained on a set of 16 custom features, while the deep learning model used speckle pattern images as input. Both types of data were standardized dataset-wise beforehand using z-score. The best machine learning and deep learning models achieved 95% accuracy. The study highlights that the nutritional similarity of the samples highly impacts the models’ confusion in both cases, especially when Campaigns conducted at different sample temperatures were not included in the training. Overall, the analysis technique presented leveraging uncertainty metrics is a stepping stone toward relevant advances in the field of milk analysis.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119246"},"PeriodicalIF":5.6,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145247842","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 phase space and network-based approach for diagnosing compensation capacitor faults in Jointless Track Circuits","authors":"Guangwu Chen,&nbsp;Shilin Wang,&nbsp;Peng Li,&nbsp;Xin Zhou,&nbsp;Shilin Zhao,&nbsp;Jianqiang Shi,&nbsp;Chengqi Bao","doi":"10.1016/j.measurement.2025.119262","DOIUrl":"10.1016/j.measurement.2025.119262","url":null,"abstract":"<div><div>The reliability of compensation capacitors in Jointless Track Circuits (JTCs) is critical for maintaining stable signal transmission and ensuring train operation safety. However, these components are prone to degradation due to long-term use and environmental disturbances, leading to potential signal anomalies. Traditional diagnostic methods often fail to cope with the nonlinear, time-varying, and multi-fault characteristics of track circuit signals. This paper proposes a novel fault diagnosis approach that integrates phase space reconstruction with complex network analysis. First, one-dimensional track signals are mapped into a high-dimensional phase space to reveal dynamic behaviors. Singular Value Decomposition (SVD) is applied to the trajectory matrix for dominant feature extraction. A complex network is then constructed from the processed signal, and key topological metrics — such as degree centrality, clustering coefficient, betweenness centrality, and local structure entropy — are computed to identify fault-related patterns. Experimental validation using real-world JTC signal data demonstrates that the proposed method achieves superior diagnostic accuracy and robustness compared to conventional techniques. Notably, the clustering coefficient proves highly sensitive in differentiating between healthy and faulty conditions. The proposed framework offers a scalable and effective solution for early fault detection and real-time condition monitoring in railway signal systems.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119262"},"PeriodicalIF":5.6,"publicationDate":"2025-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145270136","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":"Detection of concrete bridge surface damage using wavelet-based multiband channel attention mechanism","authors":"Zhangli Lan ,&nbsp;Xin Ma ,&nbsp;Hong Zhang ,&nbsp;Weihong Huang ,&nbsp;Chuanghan He ,&nbsp;Xi Xu","doi":"10.1016/j.measurement.2025.119255","DOIUrl":"10.1016/j.measurement.2025.119255","url":null,"abstract":"<div><div>This study proposes a dual improvement strategy and constructs a dedicated dataset to address the challenges of insufficient feature extraction, information loss, and high model complexity in detecting concrete bridge surface damage. A wavelet-based multiband channel attention mechanism (WMCAM) is developed to establish channel-wise attention weights through multiscale analysis of feature responses across different frequency bands, significantly enhancing damage feature extraction in complex backgrounds. Furthermore, an innovative compound convolutional fusion module (C2f-BNS) is introduced, integrating channel shuffle and pointwise convolution to enhance cross-channel information exchange while reducing model parameters by 53.6%. To overcome the limitations of existing datasets, the Chongqing concrete bridge surface damage dataset (CCBSD) is constructed. This dataset comprises 7,243 high-resolution images with expert annotations for four typical defect categories: CorrosionStain, ExposedBars, Efflorescence and Spallation. Experimental results demonstrate that the improved model achieves 72.9% in mAP50 on the CCBSD dataset, with the WMCAM and C2f-BNS modules contributing 2.4% and 1.4% performance gains, respectively. The proposed method effectively balances detection accuracy and computational efficiency through a 53.6% parameter reduction, providing a novel technical pathway for intelligent bridge inspection. This work aligns with practical engineering requirements whilst advancing computer vision applications in infrastructure health monitoring, particularly through its frequency-aware attention mechanism and lightweight architecture design.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119255"},"PeriodicalIF":5.6,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145270176","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 lightweight bearing defect detection model suitable for industrial scenarios","authors":"Biao Zhang,&nbsp;Rongke Xun,&nbsp;Jiazhong Xu","doi":"10.1016/j.measurement.2025.119239","DOIUrl":"10.1016/j.measurement.2025.119239","url":null,"abstract":"<div><div>Bearings play a crucial role in mechanical systems, but surface defects can severely impact their lifespan and reliability, making defect detection vital for safe operations. However, existing bearing defect detection models suffer from issues in complex industrial settings, including high model complexity, significant computational resource consumption, and limited capability in identifying multi-directional, small-scale defects. To address these issues, this paper proposes a lightweight defect detection model for bearings, named LARD-YOLOv8, based on the YOLOv8n architecture. The model features a LiteShiftHead detection head with SPConv, REG, and CLS modules for efficient feature extraction and accurate classification regression while keeping the model lightweight. The ARConv module enhances adaptability to multi-directional defects through a convolutional kernel rotation mechanism and dynamic weight adjustment. The RepNCSPELAN4 module’s reparameterization technique further optimizes computational efficiency. Additionally, the Inner-DIoU loss function, with its dynamic adjustment of auxiliary bounding boxes, improves localization accuracy and convergence speed. Experimental results demonstrate that LARD-YOLOv8 achieves 96.3 % accuracy, 96.2 % recall, 98.4 % mAP0.5, and 71.1 % mAP0.5:0.95 on the bearing defect dataset (BR-DET). representing improvements of 2.4 %, 4.2 %, 2.1 %, and 6.0 % respectively over YOLOv8n. Concurrently, the model reduces parameter count by 19.5 % and computational load by 13.4 %, while maintaining a real-time detection speed of 89 FPS, meeting industrial inspection timeliness requirements. Moreover, compared to mainstream models such as YOLOv11n and YOLOv12n, LARD-YOLOv8 demonstrates significant advantages across all performance metrics. Cross-domain validation on public datasets including Northeastern University’s NEU-DET and DST-DET further confirms that this model possesses excellent generalisation capabilities and robustness while maintaining high accuracy, effectively meeting the demands of industrial real-time detection.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119239"},"PeriodicalIF":5.6,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269692","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":"Advancements in FIV-based flow measurement: full-scale experimental and LES-SSI modeling approaches for buried pipelines","authors":"Haobin Chen ,&nbsp;Ron Chik-Kwong Wong ,&nbsp;Simon Park ,&nbsp;Ron Hugo","doi":"10.1016/j.measurement.2025.119214","DOIUrl":"10.1016/j.measurement.2025.119214","url":null,"abstract":"<div><div>Flow Induced Vibration (FIV)-based measurement offers an economical, non-invasive, and readily implementable approach for monitoring pipeline flow rate and safety. Investigations into FIV-based flow measurement have, however, only been performed for above ground pipelines. The FIV characteristics of buried pipelines under various influence factors remain largely unexplored, potentially introducing significant errors in measurement outcomes. To address these gaps, this study conducts both full-scale experiments and numerical simulations of buried steel pipeline FIV. Experiments are performed characterizing FIV by varying Reynolds number (<em>Re_d</em>) and Depth of Cover (<em>DOC</em>) in proctor compacted soil. A Large-Eddy Simulation coupled with Soil-Structure Interaction (LES-SSI) modeling framework is used to simulate buried steel pipeline FIV. The LES-SSI model is validated through convergence tests and verified against both experimental and benchmark data. The study thoroughly investigates the relationship between FIV and various parameters. Subsequently, an optimal quantity equation for buried steel pipeline flow measurement is developed.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119214"},"PeriodicalIF":5.6,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145247841","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":"TSMSlopRE: time-shifted multiscale slope Rényi entropy and its application in underwater radiated noise identification","authors":"Jindong Luo ,&nbsp;Chunhua Li ,&nbsp;Qinying Zhou ,&nbsp;Chengjiang Zhou ,&nbsp;Zaili Gao ,&nbsp;Yunlu Li ,&nbsp;Huiling Li ,&nbsp;Xiyu Zhang","doi":"10.1016/j.measurement.2025.119221","DOIUrl":"10.1016/j.measurement.2025.119221","url":null,"abstract":"<div><div>Underwater radiated noise identification plays a critical role in marine monitoring and defense systems, yet remains challenging due to the limitations of existing feature extraction and classification methods. Its core lies in the construction of feature extraction and identification model. However, the existing slope entropy (SlopEn) suffers from insufficient dynamic feature characterization capability and limited multiscale analysis performance, while LSTSVM based on one-versus-one (OVO) or one-versus-all (OVA) strategies faces critical issues with class imbalance and local overfitting. Therefore, an underwater radiated noise identification method based on time-shifted multiscale slope Rényi entropy (TSMSlopRE) and directed acyclic graph LSTSVM (DAG LSTSVM) is proposed. Firstly, a time series measurement method called Slope Rényi Entropy (SlopRE) is constructed, which dynamically adjusts the sensitivity of SlopEn to probability distribution through an output method based on Rényi entropy, thereby improving the stability of entropy values. Secondly, we extend SlopRE to the multiscale domain by constructing time-shifted multiscale (TSM) coarse-grained and normalization processing strategies to ensure comprehensive and effective extraction of multiscale signal features. Then, we extend the LSTSVM to multiscale DAG LSTSVM by constructing DAG strategy, which significantly reduces the imbalance of model classification categories. Finally, we combine the proposed TSMSlopRE with the multi-classification strategy of DAG LSTSVM, and apply it to the research field of underwater radiated noise identification. Experiments have shown that the accuracy of underwater radiation noise recognition for ships and sea surface environments is as high as 97.40% and 100.00%, respectively, which has important research significance in actual marine environment investigations.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119221"},"PeriodicalIF":5.6,"publicationDate":"2025-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145247839","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":"Establishment and evaluation of dynamic hysteresis models for piezo-positioning platform with high-frequency resonance characteristics","authors":"Haotian Liu,&nbsp;Guilin Zhang","doi":"10.1016/j.measurement.2025.119240","DOIUrl":"10.1016/j.measurement.2025.119240","url":null,"abstract":"<div><div>The high-frequency resonance characteristics of the piezo-positioning platform is a crucial factor limiting the increase of servo bandwidth. This paper proposes an identification method for the linear dynamic characteristics of piezoelectric positioning platforms that can accurately identify high-frequency resonance characteristics. Firstly, based on the Prandtl-Ishlinskii model, the system’s impulse response sequence is obtained using pseudo-random binary signals and correlation function calculations. Secondly, the Hankel matrix is constructed using the impulse response sequence, and the system’s order is directly determined through singular value decomposition, with the model parameters obtained via the eigensystem realization algorithm. Then, based on the system’s frequency response model and the nominal model, a method is proposed to evaluate the quality of the linear dynamic model using multiplicative uncertainty errors and <em>v-Gap</em>. Finally, the experimental results verify the effectiveness of the proposed identification method and model evaluation method.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119240"},"PeriodicalIF":5.6,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145247838","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":"Density measurement of gravel clay material of core wall dam with experiment and numerical simulation based on the additional mass method","authors":"Xiang Yu ,&nbsp;Yongguang Fu ,&nbsp;Minghao Li ,&nbsp;Feng Wang","doi":"10.1016/j.measurement.2025.119242","DOIUrl":"10.1016/j.measurement.2025.119242","url":null,"abstract":"<div><div>The compaction density of dam construction material is a key indicator for assessing dam fill quality. Although the additional mass method has been widely used for rockfill density measurement, research on gravel clay material is still limited. Furthermore, the traditional method assumes constant stiffness and vibration mass of rockfill under impact loads, which deviates from the actual situation, and further research is urgently needed. In this paper, a new density measurement method based on additional mass method and numerical simulation is proposed. First, impact force and dynamic response tests were conducted by the additional mass method model test. The effect of the additional mass on the propagation of the impact signal was investigated, and the response characteristics of gravel clay under impact were analyzed. Subsequently, a three-dimensional numerical model is established and the accuracy is verified comparing with the experimental results. The vibration range and dominant frequency of gravel clay are obtained combined with the results of model test and numerical simulation. Based on the variation patterns of vibration mass and dominant frequency, a density-fitting relationship was formulated. The actual vibration mass of the gravel clay was obtained from the fitting results, and the density was subsequently calculated. Finally, the engineering example is used to verify that the calculation error can be controlled within 3%, which proves the feasibility and accuracy of the method. This method fills the application gap of numerical simulation in this field, significantly improves the efficiency and accuracy, and provides a novel strategy for density measurement.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119242"},"PeriodicalIF":5.6,"publicationDate":"2025-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145247836","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":"RAP-DETR: Enhancing RT-DETR for Railway Track Defect Detection","authors":"Hui Xie,&nbsp;Huibo Zhou,&nbsp;Ruolan Chen,&nbsp;Bingyang Wang","doi":"10.1016/j.measurement.2025.119058","DOIUrl":"10.1016/j.measurement.2025.119058","url":null,"abstract":"<div><div>To safeguard the smooth operation of railway transportation, this paper proposes an improved RT-DETR railroad track defect detection algorithm, RAP-DETR, which detects defects such as scratches, burns, surface wear, and fractures that may occur during railway operation. Firstly, the backbone network is streamlined by integrating the CSP-RAB module for multi-scale feature fusion, which not only enhances denoising performance but also lowers resource consumption. . Secondly,to augment the Attention-based Intra-scale Feature Interaction (AIFI) module, learnable positional coding is introduced to enhance the detection efficiency by dynamically adapting the positional encoding. Finally, the Pinwheel-shaped Convolution (PSConv) module, which is based on a novel windmill-style convolution, is put forward. It effectively refines the standard convolution operation, substantially expands the receptive field, and facilitates the enhancement of feature extraction. As a result, a higher detection accuracy is achieved. Experimental validation conducted on the RailDefect dataset demonstrates that the [email protected] of RAP-DETR reaches 84.8%, representing a 4.4% improvement compared to the original RT-DETR. Moreover, the precision and recall rates of the model have increased by 2.3% and 4.7%, respectively. Meanwhile,its parameter count has decreased by 35.2%, and the number of floating-point operations (FLOPs) has been reduced by 7.54%. These notable improvements underscore the robust capability of the proposed model to effectively detect defects on railway tracks. The RailDefect dataset is publicly available at <span><span>https://github.com/0317cellxie/RailDefect</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"258 ","pages":"Article 119058"},"PeriodicalIF":5.6,"publicationDate":"2025-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145269080","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}