Measurement最新文献_第10页

IALF-YOLO: Insulator defect detection method combining improved attention mechanism and lightweight feature fusion network IALF-YOLO：结合改进的关注机制和轻量级特征融合网络的绝缘子缺陷检测方法

IF 5.2 2区工程技术

Measurement Pub Date : 2025-05-03 DOI: 10.1016/j.measurement.2025.117701

Zhiyu Mei , Hongzhen Xu , Liyue Yan , Kafeng Wang

{"title":"IALF-YOLO: Insulator defect detection method combining improved attention mechanism and lightweight feature fusion network","authors":"Zhiyu Mei , Hongzhen Xu , Liyue Yan , Kafeng Wang","doi":"10.1016/j.measurement.2025.117701","DOIUrl":"10.1016/j.measurement.2025.117701","url":null,"abstract":"<div><div>Effect and efficient insulator defect detection is critical for advancing smart grid technologies. Current deep learning-based methods face limitations in small-object recognition accuracy, insufficient key feature extraction, and high computational complexity, which restrict their application in grid inspections. We propose IALF-YOLO, an improved YOLOv5s-based model, to address these problems. Firstly, by fusing the shallow feature map of Backbone and the deep feature map of Neck, a detection layer dedicated to small objects is created in Head, significantly improving small objects’ detection accuracy. Secondly, a S-CBAM attention mechanism is proposed, which addresses the issue of feature information loss in conventional CBAM by synchronizing the extraction channel with spatial attention. Finally, the lightweight GSConv module replaces the convolutional layer in the Neck network to construct a lightweight feature fusion network, which improves detection accuracy while reducing model complexity and the number of parameters. Our method improves mAP by 2.8% and 2.5% on both datasets, respectively. The detection speed is 2<span><math><mo>×</mo></math></span> faster than other methods.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117701"},"PeriodicalIF":5.2,"publicationDate":"2025-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143912740","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}

引用次数: 0

OTDR signature of polymer optical fiber for deformation monitoring 用于变形监测的聚合物光纤OTDR特征

IF 5.2 2区工程技术

Measurement Pub Date : 2025-05-03 DOI: 10.1016/j.measurement.2025.117757

Wenbin Hu , Jiangpeng Zhou , Siyu Liang , Daniele Tosi , Minghong Yang

引用次数: 0

Analysis of standard uncertainty using the Monte Carlo method for arc measurement on a coordinate measuring machine 三坐标测量机上圆弧测量的蒙特卡罗法标准不确定度分析

IF 5.2 2区工程技术

Measurement Pub Date : 2025-05-02 DOI: 10.1016/j.measurement.2025.117589

Sergey N. Grigoriev , Oleg V. Zakharov , Shengyu Shi , Dmitriy A. Masterenko , Tatyana N. Ivanova

引用次数: 0

Accurate decomposition of complex multi-component nonstationary signals of rotating machinery: A novel polynomial chirp mode decomposition approach 旋转机械复杂多分量非平稳信号的精确分解：一种新的多项式啁啾模态分解方法

IF 5.2 2区工程技术

Measurement Pub Date : 2025-05-02 DOI: 10.1016/j.measurement.2025.117743

Bo Fu , Rongchuan Wu , Yi Quan , Chaoshun Lic , Xilin Zhao

{"title":"Accurate decomposition of complex multi-component nonstationary signals of rotating machinery: A novel polynomial chirp mode decomposition approach","authors":"Bo Fu , Rongchuan Wu , Yi Quan , Chaoshun Lic , Xilin Zhao","doi":"10.1016/j.measurement.2025.117743","DOIUrl":"10.1016/j.measurement.2025.117743","url":null,"abstract":"<div><div>In complex operating environments, the monitoring signals of rotating machinery often exhibit significant nonlinearity and non-stationarity, which creates major challenges for fault diagnosis. Although adaptive chirp mode decomposition (ACMD) offers high time–frequency resolution, its performance deteriorates with inaccurate initial instantaneous frequency (IF) estimation and shows limitations when processing signals with crossing IFs. To address these issues, this paper proposes a novel polynomial chirp mode decomposition (PCMD) method to enhance decomposition accuracy for complex multi-component signals. Firstly, we propose an adaptive IF optimization scheme (APIFO) based on the polynomial chirplet transform (PCT), which adaptively determines the fitting order of PCT in accordance with the error-adjusted R-Squared value to achieve accurate estimation of the IF. Secondly, based on demodulation techniques, we introduce an instantaneous amplitude (IA) chirp tracking filter to reconstruct the signal modes and extract the IA using the IF provided by APIFO. Finally, we propose an IA correction strategy for signals with crossing IFs. This strategy first classifies the signal modes by the absolute value of the second derivative of IA and then corrects the modes of different categories through weighted fitting or LSTM neural network methods. Simulation and experimental results demonstrate that the PCMD method provides superior IF estimation and accurate mode decomposition performance compared with classical techniques in rotating machinery fault diagnosis applications.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117743"},"PeriodicalIF":5.2,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143947142","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}

引用次数: 0

Towards safer robot-assisted skull bone drilling: A real-time force model under an elastic clinical environment 迈向更安全的机器人辅助颅骨钻孔：弹性临床环境下的实时力模型

IF 5.2 2区工程技术

Measurement Pub Date : 2025-05-02 DOI: 10.1016/j.measurement.2025.117669

Hao Ren , Zhaowei Liang , Zhichao Li , Wenqing Ren , Xiaodong Ma , Dan Wu

{"title":"Towards safer robot-assisted skull bone drilling: A real-time force model under an elastic clinical environment","authors":"Hao Ren , Zhaowei Liang , Zhichao Li , Wenqing Ren , Xiaodong Ma , Dan Wu","doi":"10.1016/j.measurement.2025.117669","DOIUrl":"10.1016/j.measurement.2025.117669","url":null,"abstract":"<div><div>The safety of cranial surgical drilling is often challenged by the risk of complications, especially mechanical damage to soft tissues from accidental penetration, highlighting the need for a deeper understanding of this procedure. While robot-assisted drilling contributes to the stability and precision, it is short of clinical experience to understand the force-depth relationship during this process. Traditional bone drilling models working under experimental conditions are challenged by several complexities in clinical cranial utilization, fluctuating feedrate, manual interruption, and low system stiffness. To address these questions, a real-time force model is introduced in this study to predict the thrust force and the actual drilling depth, aiming to enhance the monitoring capacity in craniotomy for better safety. An offline model is included to elucidate the force generation mechanism under varied feedrate using specialized drilling tool, as an expansion of traditional research. Then a novel online prediction method complements this foundation by considering the current robot position and system stiffness, providing real-time depth and force estimation based on partial differentiation. This approach is well-suited for adjusting to manual interruptions and density variations dynamically, which reveals the pattern of force variation in the clinical environment. Experimental validation demonstrated a reasonable prediction accuracy and a submillimeter depth error, indicating the feasibility to monitor the skull drilling procedure. This capability broadens the traditional drilling force model’s application and significantly contributes to the development of multi-modal safety protocols in clinical robot-assisted skull drilling.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117669"},"PeriodicalIF":5.2,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143906687","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}

引用次数: 0

A novel method for untrained detection of compound fault in rolling bearing via fast Fourier Transform-Transformer model 基于快速傅立叶变换-变压器模型的滚动轴承复合故障非训练检测新方法

IF 5.2 2区工程技术

Measurement Pub Date : 2025-05-02 DOI: 10.1016/j.measurement.2025.117755

Jiahui Tang , Xiaole Cheng , Jian Sun , Jiajuan Qing , Peien Luo , Sheng Hu

{"title":"A novel method for untrained detection of compound fault in rolling bearing via fast Fourier Transform-Transformer model","authors":"Jiahui Tang , Xiaole Cheng , Jian Sun , Jiajuan Qing , Peien Luo , Sheng Hu","doi":"10.1016/j.measurement.2025.117755","DOIUrl":"10.1016/j.measurement.2025.117755","url":null,"abstract":"<div><div>Rotating machinery relies heavily on rolling bearings, which are vulnerable to compound faults involving multiple interacting failure modes. Traditional diagnostic methods often inadequately decouple these superimposed vibration patterns and lack adaptability to untrained fault categories. This study proposes a novel compound fault diagnosis model based on the Fast Fourier Transform-Transformer (FFT-Transformer) architecture, utilizing attention mechanisms to extract fault features from vibration signals. The model first applies FFT to isolate fault-related frequency bands, eliminating noise interference. A multi-head attention mechanism then deciphers temporal dependencies in vibration signals, enabling precise identification of coexisting faults without prior knowledge of compound patterns. Crucially, the compound fault discrimination terms dynamically classify untrained fault types by evaluating classifier confidence levels, circumventing the need for exhaustive training data. Experimental results demonstrate that the proposed method effectively identifies fault conditions absent from the training data, significantly improving diagnostic performance and model reliability. This approach represents a notable advancement in fault diagnosis for rotating machinery, offering a robust solution to the challenges of compound fault identification with minimal data requirements.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117755"},"PeriodicalIF":5.2,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902542","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}

引用次数: 0

High readability restoration of underwater-to-air text image for underwater devices based on URGAN 基于URGAN的水下设备水对空文本图像高可读性恢复

IF 5.2 2区工程技术

Measurement Pub Date : 2025-05-02 DOI: 10.1016/j.measurement.2025.117514

Ranhao Zhang , Fudong Zhang , Haoran Meng , Chuandong Jiang , Liang Wang

{"title":"High readability restoration of underwater-to-air text image for underwater devices based on URGAN","authors":"Ranhao Zhang , Fudong Zhang , Haoran Meng , Chuandong Jiang , Liang Wang","doi":"10.1016/j.measurement.2025.117514","DOIUrl":"10.1016/j.measurement.2025.117514","url":null,"abstract":"<div><div>With advancements in maritime-to-aerial reconnaissance technologies and improvements in underwater devices, restoring distorted text images captured underwater for above-water situation awareness has become a key research focus. Conventional algorithms and deep learning-based methods often struggle to achieve clear and accurate text restoration. To address this challenge, a specialized dataset of underwater distorted text images was constructed using a large-scale scene text dataset and an underwater image distortion algorithm. URGAN (Underwater-text-image Restoration Generative Adversarial Network) is introduced as the first GAN-based method specifically designed for restoring underwater distorted text images. In particular, the generator of URGAN innovatively integrates numerous residual blocks and large convolutional kernels to preserve fine details. URGAN demonstrates strong performance in restoring text details and edges. In tests on simulated data, URGAN achieved a PSNR of 18.68 dB and an SSIM of 0.57. On real-world data, URGAN achieved a PSNR of 18.30 dB, an SSIM of 0.56, and a text recovery accuracy of 79.16%. These results confirm that URGAN generates highly readable restored images, showcasing its significant potential for applications in image processing for underwater devices.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117514"},"PeriodicalIF":5.2,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143921960","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}

引用次数: 0

Using handheld 3D laser scanner and high-resolution handheld digital microscope for hybrid building condition measurements 使用手持式三维激光扫描仪和高分辨率手持式数字显微镜进行混合建筑条件测量

IF 5.2 2区工程技术

Measurement Pub Date : 2025-05-01 DOI: 10.1016/j.measurement.2025.117751

Czesław Suchocki , Rafał Nowak , Radosław Rutkowski , Wioleta Błaszczak-Bąk

{"title":"Using handheld 3D laser scanner and high-resolution handheld digital microscope for hybrid building condition measurements","authors":"Czesław Suchocki , Rafał Nowak , Radosław Rutkowski , Wioleta Błaszczak-Bąk","doi":"10.1016/j.measurement.2025.117751","DOIUrl":"10.1016/j.measurement.2025.117751","url":null,"abstract":"<div><div>In the diagnostic measurement of buildings and engineering structures, precise measurements are essential, particularly for detecting defects that, despite their small size, can significantly impact the safety of the structure. Terrestrial laser scanning (TLS) is a well-established measurement technology for buildings and structures. However, TLS has limitations in accurately measuring very small defects, such as cracks narrower than 1 mm, which necessitates the use of supplementary technologies. This paper presents an innovative approach to the comprehensive measurement of small cracks and defects by integrating a handheld 3D laser scanner (HLS) and high-resolution handheld digital microscope (HDM) as complementary tools for TLS measurements. Combining these technologies enables the collection of comprehensive data across multiple scales with varying levels of accuracy, from large areas to microscopic details, thereby enhancing diagnostic precision and efficiency in civil engineering applications. To address the mobility limitations of HLS measurements, a custom-designed backpack was developed and manufactured to facilitate such studies. The proposed hybrid approach to surveying building structures proved to be efficient, precise, reliable, and fast, ultimately offering a robust solution for advanced building diagnostics in civil engineering.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117751"},"PeriodicalIF":5.2,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143904163","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}

引用次数: 0

Soft microtubular sensors as artificial fingerprints for incipient slip detection 软微管传感器作为早期打滑检测的人工指纹

IF 5.2 2区工程技术

Measurement Pub Date : 2025-05-01 DOI: 10.1016/j.measurement.2025.117729

Longteng Yu , Wuxin Xiao , Qi Wang , Dabiao Liu

{"title":"Soft microtubular sensors as artificial fingerprints for incipient slip detection","authors":"Longteng Yu , Wuxin Xiao , Qi Wang , Dabiao Liu","doi":"10.1016/j.measurement.2025.117729","DOIUrl":"10.1016/j.measurement.2025.117729","url":null,"abstract":"<div><div>Incipient slip detection constitutes a crucial aspect of adaptive grasping and dexterous manipulation in robotics. The primary challenge lies in the subtle nature of incipient slip across temporal, spatial, and force dimensions. This work reports a soft robotic finger capable of accurately detecting incipient slip using artificial fingerprints composed of two piezoresistive microtubular sensors. Experimental results reveal distinctive peak patterns in the sensing signals during incipient slip on smooth and rough surfaces. For smooth surfaces, the direction of slip can be determined by the opposite changing trends in the sensing signals. Finite element analysis elucidates that the underlying mechanisms are driven by the asymmetric local geometry around the sensors when sliding on a smooth surface, and by the relative position of the sensors to the surface micro-structure when sliding on a rough surface. A customized program is then developed for real-time incipient slip detection based on peak recognition in de-noised rolling windows. The feasibility of this method is demonstrated through the adaptive grasping of deformable, moving, and weight-unknown objects using a robotic hand integrated with the soft tactile fingers.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117729"},"PeriodicalIF":5.2,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143904164","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}

引用次数: 0

Non-destructive detection method for nonlinear anchorage state of prestressed anchor cables based on the MSVAR model 基于MSVAR模型的预应力锚索非线性锚固状态无损检测方法

IF 5.2 2区工程技术

Measurement Pub Date : 2025-05-01 DOI: 10.1016/j.measurement.2025.117744

Hui Cao , Hao Wang , Xinghua Chen

{"title":"Non-destructive detection method for nonlinear anchorage state of prestressed anchor cables based on the MSVAR model","authors":"Hui Cao , Hao Wang , Xinghua Chen","doi":"10.1016/j.measurement.2025.117744","DOIUrl":"10.1016/j.measurement.2025.117744","url":null,"abstract":"<div><div>With the growing application of prestressed anchor cables in engineering, accurately assessing their anchoring status has become essential. This study proposes a novel vibration-based non-destructive method using the Markov-Switching Vector Autoregressive (MSVAR) model to characterize anchorage conditions. By analyzing vibration signals, the method identifies nonlinear behavior associated with anchoring force and grout defects. Three scaled specimens replicating typical anchoring states were tested. The MSVAR model revealed hidden state transitions, and information entropy was introduced to quantify the degree of nonlinearity. Nonlinear coefficients, determined via density peak clustering (DPC), were found to correspond with anchorage force. A rapid detection approach was established by analyzing the slope of nonlinear coefficients in the over-tension stage, enabling efficient tension estimation. Field validation showed that the nonlinear coefficients increased with tension but with diminishing growth rates. During loading and unloading, coefficients remained nearly constant at the same tension levels, indicating stability. Compared to the Hilbert transform method, the MSVAR-DPC approach achieved a 53 % improvement in prediction accuracy and reduced processing time by 63 %. This confirms its robustness and adaptability under complex field conditions. As the first approach to introduce an entropy-based nonlinear coefficient for evaluating anchorage force, this method enhances detection sensitivity and reliability while addressing limitations of traditional techniques. It provides a new paradigm for real-time health monitoring of anchor cables in practical engineering applications.</div></div>","PeriodicalId":18349,"journal":{"name":"Measurement","volume":"253 ","pages":"Article 117744"},"PeriodicalIF":5.2,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143922927","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}

引用次数: 0