IEEE Transactions on Instrumentation and Measurement最新文献

{"title":"A Cross-Domain Online Diagnosis Framework Under Small Fault Sample","authors":"Zuoshuang Chen;Dongdong Zhang;Zuoyi Chen;Jun Wu;Hong-Zhong Huang","doi":"10.1109/TIM.2025.3606021","DOIUrl":"https://doi.org/10.1109/TIM.2025.3606021","url":null,"abstract":"Cross-domain fault diagnosis in industrial applications presents a major challenge, especially when only a limited number of fault samples are available. Existing data-driven and transfer learning (TL) methods often struggle with real-time diagnosis, insufficient generalization across domains, and the inability to adapt to continuously evolving fault conditions. To address these limitations, this article proposes a novel cross-domain online fault diagnosis framework (CDODF). The framework leverages the contrastive language-image pretraining (CLIP) model to extract robust, domain-invariant features from limited fault data. To further enable cross-domain adaptation without costly fine-tuning, a lightweight Adapter module is introduced, which incorporates few-shot learning and online adaptation to target-domain features. Moreover, CDODF supports a continuous learning strategy that dynamically updates the model using accumulated target-domain data, ensuring long-term adaptability and diagnostic accuracy. Experimental results across scenarios, including cross-working conditions, cross-device diagnosis, and emerging fault types, show that CDODF consistently outperforms existing deep learning (DL), TL, and few-shot methods.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.9,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036727","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":"Position-Aware Self-Supervised Learning for Wafer Map Defect Pattern Recognition","authors":"Wei Yuan;Jinda Yan;Minghao Piao","doi":"10.1109/TIM.2025.3606012","DOIUrl":"https://doi.org/10.1109/TIM.2025.3606012","url":null,"abstract":"Wafer map defect pattern recognition is an indispensable component of semiconductor manufacturing, providing crucial information for identifying the root causes of defects in semiconductor production. In recent years, to address the overreliance on labeled data in supervised learning approaches, some efforts have introduced the concept of self-supervised learning into wafer map defect pattern recognition. However, these studies often ignore the significant data characteristics related to the spatial location of defect clusters on the wafer map itself. To address this issue, we designed an RingDistanceConv (RDConv) module to consider the impact of two types of position information—coordinates and distances—on wafer map defect recognition and proposed the position-aware self-supervised learning framework. Our framework achieved an accuracy of 96.41% on the WM-811K dataset with eight defect classes.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.9,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036797","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":"EWHT-AIB: Enhanced Waist-Mounted Human Tracking Framework Based on Array IMU and Barometer","authors":"Feifan Lin;Qingzhong Cai;Yue Yu;Huizheng Yuan","doi":"10.1109/TIM.2025.3604120","DOIUrl":"https://doi.org/10.1109/TIM.2025.3604120","url":null,"abstract":"With the development of the Internet of Things (IoT) and artificial intelligence (AI), indoor location-based services have become an indispensable part of public daily life. The performance of 3-D indoor positioning is constrained by the low performance of consumer-grade micro-electromechanical systems (MEMS) inertial measurement unit (IMU), the lack of effective calibration for the barometer, and the poor adaptability to complex human motion modes. To address the above challenges, this article proposes an enhanced waist-mounted human tracking framework based on array IMU and barometer (EWHT-AIB) that combines array IMU data fusion, precise barometer calibration, and a motion-constrained position-attitude update algorithm to achieve robust and accurate indoor positioning. To enhance array IMU data fusion performance, a weighted data fusion algorithm for array IMU based on the bias instability coefficients is proposed to achieve effective weighted fusion of array IMU data. Subsequently, a barometer calibration algorithm based on nonlinear fitting is proposed to achieve accurate compensation for bias error and scale factor error of the barometer. Finally, a position-attitude update algorithm under motion constraints is designed to achieve accurate pedestrian 3-D indoor positioning using compensated array IMU and barometer data. Comprehensive experiments demonstrate that the proposed EWHT-AIB framework can achieve meter level positioning accuracy under typical indoor environments.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.9,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036925","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 Decoupled Hierarchical Fault Detection Method for Insulator Parallel Gaps: Integrating Lightweight Localization and Attention-Based Diagnosis","authors":"Shuai Hao;Tianqi Li;Xu Ma;Shiao Fan;Tianrui Qi","doi":"10.1109/TIM.2025.3604115","DOIUrl":"https://doi.org/10.1109/TIM.2025.3604115","url":null,"abstract":"Insulator parallel gaps, as critical lightning protection components in high-voltage transmission lines, are prone to faults, such as short circuits and excessive spacing, which compromise line safety and power supply reliability. However, detecting insulator parallel gaps from unmanned aerial vehicle (UAV) captured images is challenged by complex backgrounds, structural distortion, and occlusion. Thus, a hierarchical detection method incorporating lightweight localization and attention mechanism-based diagnosis (KLSD-HDet) is proposed, which uses a localization network to capture the faults and then conducts fault diagnosis. First, to accurately capture target objects, a lightweight fault localization network (KDeFus-LNet) is designed, with nonlinear feature extraction (NFE) and target localization capabilities in complex backgrounds enhanced. Second, leveraging the spatial geometric information from 3-D fault models, a multiview fault image generation method is developed to compensate for the missing feature representations of partial viewpoints in real-world datasets. Then, a cross-space learning and multiscale residual-based fault diagnosis network (S-CR2-DNet) is proposed to improve multiview fault representation understanding and diagnostic accuracy. Finally, knowledge distillation is employed to lightweight S-CR2-DNet, enhancing its practical applicability. Extensive experiments validate that KLSD-HDet outperforms the state-of-the-art (SOTA) methods, achieving 94.49% detection precision, improved by 6.65% compared to the SOTA algorithms.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-15"},"PeriodicalIF":5.9,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036751","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":"Study on Temperature Measurement Errors and Correction Method of Inner Surface of Cavity-Like Objects With Directional Reflection Characteristics","authors":"Ao Zhang;Ailin Xie;Guohui Mei;Shumao Zhao;Wu Lv","doi":"10.1109/TIM.2025.3604941","DOIUrl":"https://doi.org/10.1109/TIM.2025.3604941","url":null,"abstract":"The thermal radiation reflection properties of real material surfaces tend to be directional, which leads to inaccuracies in existing methods for measuring the diffuse inner surface temperature of cavity-like objects. In this article, we propose a method for measuring the temperature at the inner surface of cavity-like objects that describes the interreflection based on the bidirectional reflectance distribution function (BRDF) and on the Monte Carlo ray-tracing method (MCM). The effects of measurement angles and surface roughness on temperature measurement errors have been analyzed through simulation calculations and experiments. The results show that the inner surface structure and reflection characteristics make the error change at different measurement angles. Compared to the actual methods in use, the results obtained for the proposed method for the inner surface with directional reflection allow a reduction in the maximum relative error from 14.2% to 0.8%.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-10"},"PeriodicalIF":5.9,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021386","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":"Multistrategy Progressive Adaptation for Generalized Open-Set Cross-Working Condition Fault Diagnosis in Rotating Machinery","authors":"Longde Wang;Hui Cao;Tianjian Wang;Zeren Ai;Henglong Shen","doi":"10.1109/TIM.2025.3604981","DOIUrl":"https://doi.org/10.1109/TIM.2025.3604981","url":null,"abstract":"In real-world industrial environments, frequent condition changes, mechanical degradation, and incomplete fault labeling often lead to data distribution shifts and label space asymmetry between the source and target domains. Moreover, compounded by the emergence of previously unseen fault types, severely undermine the generalization capability of conventional intelligent diagnostic methods. Although existing open-set domain adaptation (OSDA) methods attempt to address unknown classes, most still rely on the assumption of full label space consistency, limiting their applicability under complex and uncertain industrial conditions. To overcome these limitations, this article proposes a novel model for generalized open-set cross-working condition fault diagnosis, named multistrategy progressive adaptation network (MSPAN). The model allows for partial class overlap between source and target domains, each containing private classes, thereby reducing reliance on strict label alignment, complete class coverage, and prior knowledge of the target domain. This significantly enhances the model’s adaptability and flexibility under realistic operating conditions. MSPAN integrates three core strategies: centroid-guided expansion (CGE), progressive consensus filtering (PCF), and localized knowledge integration (LKI). CGE expands the label space with pseudo-target domain samples, alleviating interference from private classes in the source domain; PCF combines dual measure-driven ranking and consensus-aware estimation to adaptively filter unknown classes in the target domain; LKI focuses on learning unknown-class representations while enhancing the transferability of general diagnostic knowledge across domains. Extensive experiments on the PU and NLN-ESP public datasets demonstrate that MSPAN achieves both accurate shared-class diagnosis and effective unknown-class separation under varying degrees of class overlap and openness. These results validate its robustness, generalization ability, and practical potential for deployment in complex industrial fault diagnosis scenarios.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.9,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145011340","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":"Displacement Monitoring and Damage Diagnosis of a Composite Suspension Control Arm Using Inverse Finite Element Method","authors":"Mohammad Amin Abdollahzadeh;Emre Komurcu;Mehmet Yildiz;Adnan Kefal","doi":"10.1109/TIM.2025.3604915","DOIUrl":"https://doi.org/10.1109/TIM.2025.3604915","url":null,"abstract":"The intermediate link that connects the chassis of a car to the body is called the “control arm.” This component ensures the safety of the front suspension of motor vehicles, which is why monitoring its structural condition is a must. In this study, displacement monitoring (also known as “shape sensing”) and damage detection and localization of a twist beam are performed to ensure the high structural health of automotive components during operation. For this purpose, we use a superior sensing algorithm based on sensor data, the inverse finite element method (iFEM), which can predict shape changes in real time and perform damage diagnosis in the entire structural domain. The iFEM formulation is based on the most widely used inverse element in this field, the inverse four-node shell (iQS4). First, the sensor placement model of the composite control arm is investigated by numerical iFEM/iQS4 analysis to embed the fiber Bragg grating (FBG) sensors at optimal positions in the laminate. Then, an experimental iFEM analysis (with physical sensor data) is performed to verify the numerical iFEM results, and a damage identification analysis is performed with the verified numerical strain data. In the final step, the numerical and experimental results are compared holistically to investigate the applicability of iFEM for vehicle components. The results of this comparison show the high precision of the real-time iFEM/iQS4 deformation reconstruction of the control arm and demonstrate the superior capabilities of damage detection and localization.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.9,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036752","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 Survey of Vision-Based Tactile Sensors: Hardware, Algorithm, Application, and Future Direction","authors":"Keshi He","doi":"10.1109/TIM.2025.3604922","DOIUrl":"https://doi.org/10.1109/TIM.2025.3604922","url":null,"abstract":"Since vision-based tactile sensors (VBTSs) were first proposed and developed, they have attracted more and more attention in recent years. This article aims to provide a comprehensive review of VBTSs, with emphasis on their algorithms and applications. First, I briefly introduce the concept, principle, and prototype of VBTSs. Then, I illustrate the deployment of VBTSs into robotic systems and discuss the state-of-the-art progress of VBTS-based robotic grasping and manipulation, then outline the main functions of VBTSs, and provide a systematic survey on their algorithms, with a focus on deep learning and computer vision. Furthermore, I summarize the applications of VBTSs in practical scenarios. Finally, I discuss key research challenges and envision future directions for VBTSs.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-21"},"PeriodicalIF":5.9,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036754","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":"Optimized Lattice-Structured Flexible EIT Sensor for Tactile Reconstruction and Classification","authors":"Huazhi Dong;Sihao Teng;Xu Han;Xiaopeng Wu;Francesco Giorgio-Serchi;Yunjie Yang","doi":"10.1109/TIM.2025.3604919","DOIUrl":"https://doi.org/10.1109/TIM.2025.3604919","url":null,"abstract":"Flexible electrical impedance tomography (EIT) offers a promising alternative to traditional tactile sensing approaches, enabling low-cost, scalable, and deformable sensor designs. Here, we propose an optimized lattice-structured flexible EIT tactile sensor incorporating a hydrogel-based conductive layer, systematically designed through 3-D coupling field simulations (3D-CFSs) to optimize structural parameters for enhanced sensitivity and robustness. By tuning the lattice channel width and conductive layer thickness, we achieve significant improvements in tactile reconstruction quality and classification performance. Experimental results demonstrate high-quality tactile reconstruction with correlation coefficients (CCs) up to 0.9275, peak signal-to-noise ratios (PSNRs) reaching 29.0303 dB, and structural similarity indexes up to 0.9660, while maintaining low relative errors down to 0.3798. Furthermore, the optimized sensor accurately classifies 12 distinct tactile stimuli with an accuracy reaching 99.6%. These results highlight the potential of simulation-guided structural optimization for advancing flexible EIT-based tactile sensors toward practical applications in wearable systems, robotics, and human–machine interfaces (HMIs). All data are publicly available in Edinburgh DataShare with the identifier <uri>https://doi.org/10.7488/ds/7982</uri>","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-9"},"PeriodicalIF":5.9,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027946","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":"Accurate High-Speed Equivalent-Time Sampling Receiver: Architecture and Performance Metrics","authors":"Aaron D. Pitcher;Charl W. Baard;Mihail S. Georgiev;Natalia K. Nikolova","doi":"10.1109/TIM.2025.3604952","DOIUrl":"https://doi.org/10.1109/TIM.2025.3604952","url":null,"abstract":"An equivalent-time (ET) sampling ultra-wideband (UWB) dual-channel receiver is proposed, which is controlled by a field-programmable gate array (FPGA). It has a programmable repetition period and ET sampling rate [up to 20 gigasamples per second (GSa/s)]. The architecture employs a programmable delay chip (PDC) to achieve ultra-high speed of over 8900 traces/s for a typical <inline-formula> <tex-math>$1~mu $ </tex-math></inline-formula>s repetition period. Compared with previously reported high-speed (PDC-based) receivers, it offers superior time-sampling accuracy. The design incorporates a custom dual-channel radio frequency (RF) front end with a low-jitter clock source, critical in achieving time-sampling stability. Importantly, a simple yet effective method is proposed to correct the systematic timebase distortions due to the PDC, whose delay inaccuracies are the main signal-degradation factor in ET receivers realizing picosecond sampling intervals. The realized low-cost system operates as a high-speed oscilloscope with a 10-dB receiver bandwidth of 6 GHz and with accuracy comparable to that of bench-top high-speed oscilloscopes. Performance metrics and measurement procedures are proposed to evaluate and compare time-sampling receivers. These are applied to the proposed receiver, including tests as part of a compact pulsed radar. Its performance is compared with two high-speed bench-top oscilloscopes as well as previously reported ET receiver prototypes.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-15"},"PeriodicalIF":5.9,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11147170","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145036781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}