IEEE Transactions on Instrumentation and Measurement最新文献

{"title":"Enhanced 1-D Convolutional Neural Network-Based Open-Circuit Fault Diagnosis and Hybrid Fault-Tolerant Control for Three-Level NPC Converters","authors":"Wei Luo;Zhipeng Xie;Yikai Li;Man Chen;Rufei He;Yumin Peng;Xin Zhang","doi":"10.1109/TIM.2025.3582333","DOIUrl":"https://doi.org/10.1109/TIM.2025.3582333","url":null,"abstract":"This article addresses the challenge of diagnosing open-circuit faults in power devices within three-level neutral point clamped (NPC) converters. An enhanced 1-D convolutional neural network (1D-CNN)-based fault diagnosis method is proposed. The method begins with the acquisition of fault data, including three-phase voltages on the dc side and three-phase output currents. A fault feature matrix is constructed using the complementary ensemble empirical mode decomposition (CEEMD) algorithm, followed by energy percentage feature extraction. This matrix is then processed by the enhanced 1D-CNN framework, which effectively detects both single and dual open-circuit faults in power devices. To address single open-circuit faults, a fault-tolerant control strategy based on space vector pulsewidth modulation (SVPWM) is introduced, ensuring continuous operation by adjusting the space vector upon fault detection. In addition, a redundant power unit is employed to maintain consistent output voltage and current amplitude during fault-tolerant operations. The proposed method’s effectiveness is validated through simulation and experimental results, demonstrating its capability to accurately locate faulty devices and enable fault-tolerant operation. This research presents a reliable solution for open-circuit fault diagnosis, improving the resilience and operational efficiency of three-level NPC converters.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-14"},"PeriodicalIF":5.6,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519413","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 and Effective Geometric Error Compensation for Ultrahigh-Precision Coordinate Measuring Machine Using Laser Tracking Interferometer","authors":"Jian Liang;Zefeng Sun;Jiehu Kang;Shanzhai Feng;Shuyang Wang;Zongyang Zhao;Luyuan Feng;Shangyong Li;Bin Wu","doi":"10.1109/TIM.2025.3582324","DOIUrl":"https://doi.org/10.1109/TIM.2025.3582324","url":null,"abstract":"Geometric errors in coordinate measuring machines (CMMs) significantly degrade measurement accuracy. While laser tracking interferometer (LTI)-based compensation methods are widely used due to their high efficiency, the limited precision of the existing positioning techniques restricts their application in ultrahigh-precision CMMs. To address these challenges, this study introduces an accurate and efficient geometric error compensation method that utilizes LTI positioning for ultrahigh-precision CMMs. The method begins with the development of a geometric error model that employs homogeneous transformation matrices (HTMs) to map end-position deviations to error parameters. A highly accurate and robust positioning algorithm for LTI is then designed, incorporating a two-step process: initial positioning through semidefinite programming (SDP) and fine-tuning using enhanced particle swarm optimization (EPSO). After parameter identification, geometric error compensation is applied based on the established model. The test experiments were conducted on a CMM with the nominal accuracy of <inline-formula> <tex-math>$2:pm : L$ </tex-math></inline-formula> [mm]/<inline-formula> <tex-math>$400 : :mu $ </tex-math></inline-formula>m. The positioning results show that the proposed method achieves a distance mean absolute error of 0.10871, demonstrating superior precision over conventional methods. Additionally, the method exhibits excellent robustness and stability under noise interference. After compensation, precision validation results showed that the maximum detection error was reduced to <inline-formula> <tex-math>$ 0.26:mu $ </tex-math></inline-formula>m, with length measurement errors within <inline-formula> <tex-math>$0.5:pm : L$ </tex-math></inline-formula> [mm]/<inline-formula> <tex-math>$400 :: mu $ </tex-math></inline-formula>m. These results highlight substantial improvements in both measurement precision and operational performance. This research presents an effective solution for geometric error compensation in CMMs, offering enhanced performance for industrial measurement applications.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.6,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550464","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":"Using Heterogeneous Extractor to Transfer Local-Global Knowledge for Cross-Domain Rolling Bearing Fault Diagnosis","authors":"Xilin Yang;Yanting Li","doi":"10.1109/TIM.2025.3582302","DOIUrl":"https://doi.org/10.1109/TIM.2025.3582302","url":null,"abstract":"The intelligent fault diagnosis (IFD) methods obtain superior performance in ensuring the safety of mechanical systems, but varying working conditions degrade the performance of intelligent models. Fortunately, unsupervised domain adaptation (UDA) has been used to handle the bias and unannotated data. High-quality features contribute to facilitating subsequent domain alignment and enhancing diagnostic performance. This article aims to address the contradiction between using complex neural networks to extract better fault features and the resulting longer inference time. Specifically, a heterogeneous extractor is designed by integrating a pure CNN-based main network in parallel with a hybrid auxiliary network. The auxiliary network consists of a CNN and a ViT, connected in series, which extract local and global fault knowledge, respectively. Then, a training strategy is proposed to help the main branch enrich the extracted features, where the pure CNN is optimized to distinguish the hard-to-transfer samples identified by auxiliary CNN-ViT extractor. Finally, an information filter mechanism is introduced to facilitate mutual feature learning between the two branches. Experiments are constructed on two diagnosis datasets and a practical platform, where the comparison studies manifest the superiority of our method in fault diagnosis tasks under varying working conditions.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550537","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":"Neural Network-Based Position and Orientation Estimation of a Centimeter Scaled Robot Using a Localized Magnetic Field Map","authors":"Navaneeth Pushpalayam;Lee Alexander;Rajesh Rajamani","doi":"10.1109/TIM.2025.3581657","DOIUrl":"https://doi.org/10.1109/TIM.2025.3581657","url":null,"abstract":"This article develops a position and orientation estimation system for a robot moving over a plane based on the use of an actively controlled magnetic field. The position estimation system consists of two magnetic sensors on the robot and an actively controlled rotating permanent magnet. The orientation of the magnet is controlled to roughly point at the robot, and a localized magnetic field map based on a neural network is developed for a narrow region around the pointing direction of the magnet. Using the magnetic fields measured at both sensors, the radial and polar positions and the orientation of the robot are estimated using an unscented Kalman filter (UKF). The orientation of the magnet is then more finely controlled to point precisely at one of the magnetic sensors. This enables the further design of an asymptotically stable nonlinear observer that provides enhanced accuracy in the radial position estimation of the robot. Extensive experimental results are presented on the performance of the estimation system, including real-time estimation of both the moving robot’s 2-D position and its orientation.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.6,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550538","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":"Linear and Widely Linear Recursive Maximum Complex-Domain Loglikelihood Adaptive Filtering in Intricate Measurement Environments","authors":"Qizhen Wang;Gang Wang;Ying-Chang Liang","doi":"10.1109/TIM.2025.3582314","DOIUrl":"https://doi.org/10.1109/TIM.2025.3582314","url":null,"abstract":"In industrial measurements, when signal reception is affected by hardware defects or asymmetric interference, I/Q imbalance can occur, which may result in the noise exhibiting noncircular and non-Gaussian characteristics, along with significant heterogeneous probability distributions in real and imaginary parts. In this setting, previous adaptive filtering cost functions (implicitly assumed homogeneous distributions) may perform well on one part but suffer on the other heterogeneous part, leading to an overall deteriorating performance, sometimes even inferior to the mean square error (mse) criterion. This article presents an innovative criterion specifically designed for such intricate measurement environments. Leveraging the inherent diversity of the Gaussian mixture model (GMM) to accommodate any probability distribution, we model the additive noises in real and imaginary parts. Through a linear combination, the overall noise is modeled as a complex-domain noncircular GMM (CNGMM). Then, a new cost function found on the recursive maximum complex-domain loglikelihood (RMCL) of the CNGMM is derived, with its linear and widely linear (WL) algorithms. Due to the excellent adaptability of CNGMM to intricate measurement environments, the proposed cost function consistently maintains outstanding performance under noncircular, non-Gaussian, and heterogeneous noises. An analysis of mean value and mean square convergence is also conducted. Further investigation reveals that the proposed steady-state mean square deviation (SS-MSD) is always less than or equal to that of complex recursive least squares (CRLS)/WL-recursive least squares (WL-RLS), which strongly indicates that RMCL/WL-RMCL is a superior scheme in intricate measurement environments. All theoretical predictions align well with simulations.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-15"},"PeriodicalIF":5.6,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550758","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 Road Damage Detection Model Based on Improved RT-DETR for Complex Environments","authors":"Xianglong Luo;Ruchen Liu;Xibin He;Huijie Wang","doi":"10.1109/TIM.2025.3582316","DOIUrl":"https://doi.org/10.1109/TIM.2025.3582316","url":null,"abstract":"Road damage detection is crucial for maintenance and management. Timely and accurate detection improves traffic safety and extends the road service life. However, road damage in complex backgrounds is often characterized by large aspect ratios, multiple scales, and abrupt changes in direction, which greatly reduce detection a ccuracy. To solve the problem, this article proposes MMR-DETR, a road damage detection network with cross fusion of multiscale information. Specifically, for the multiscale and large aspect ratio of damage in complex backgrounds, the encoder introduces multiscale multihead self-attention (M2SA) and multiscale cross fusion (MCF) modules to learn damage information, enhancing feature representation and detection performance. Additionally, a redundant bounding box merging (RBBM) method is applied to improve localization accuracy by optimizing detection boxes. To evaluate the effectiveness of the proposed model, we conducted experiments on the UAPD, UAVPDD-2023, and RDD2022 datasets. The experimental results show that our model outperforms the existing models in terms of accuracy, recall, and mAP@0.5, exhibiting excellent detection performance and generalization. The code is available at <uri>https://github.com/Lrc-1109/MMR-DETR</uri>","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.6,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550729","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":"DisCLE-BAR: A Dynamic Region-of-Interest Guided Framework for Hand Bone Age Assessment","authors":"Jiafeng Qiu;Tian Tan;Gang Shen","doi":"10.1109/TIM.2025.3582326","DOIUrl":"https://doi.org/10.1109/TIM.2025.3582326","url":null,"abstract":"Accurate bone age assessment (BAA) is crucial for evaluating pediatric health, predicting growth, and supporting legal and athletic tasks. While deep-learning-based BAA methods have improved precision and efficiency, many produce features misaligned with bone growth stages, leading to suboptimal performance and limited interpretability. To address this, we propose DisCLE-BAR, a distillation and contrastive-learning-enhanced framework for bone age regression. DisCLE-BAR dynamically identifies key regions of interest (ROIs) in hand X-ray images, adapting to specific bone growth stages. The training of DisCLE-BAR involves two phases: a full ROIs capturing phase, where knowledge distillation identifies regions critical for BAA, and an adaptive ROI weight adjustment phase, where weighted supervised contrastive learning (WSCL) refines attention maps. This design focuses the model on the most significant ROIs while minimizing the influence of less relevant areas. Experiments on the RSNA dataset show that DisCLE-BAR achieves a mean absolute error (MAE) of 3.73 months, outperforming other state-of-the-art methods by effectively capturing dynamic bone development characteristics. The results demonstrate that DisCLE-BAR offers a reliable, interpretable solution for BAA with strong clinical potential.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.6,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144519367","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 High-Sensitivity Microwave Resonator for Triclosan and Glyphosate Detection in Water","authors":"Abigail González-Zea;Arcesio Arbelaez;Abraham Ulises Chávez-Ramírez;Jose-Luis Olvera-Cervantes;José Herrera-Celis","doi":"10.1109/TIM.2025.3581626","DOIUrl":"https://doi.org/10.1109/TIM.2025.3581626","url":null,"abstract":"This work presents the design, fabrication, and testing of a circular split ring resonator (Cir-SRR) with an embedded interdigital capacitor (IDC) for emerging contaminant (EC) detection. The IDC attached to the outer ring gap of the Cir-SRR concentrates the electric field and improves the sensitivity of the resonator. According to measurements, the IDC/Cir-SRR resonates at 1.77 GHz and achieves a quality factor Q of 295. The simulation reported a hot spot of 115.9204 dBV/m in the IDC detection zone. The resonant frequency shifts approximately 400 MHz when the liquid sample changes from water to ethanol. The sensitivity was studied using ethanol in deionized (DI) water. An average sensitivity of 1.53% was achieved in the relative permittivity range of 18–79. Triclosan and glyphosate were selected as ECs and were tested by dissolving them in a 50% ethanol-water mixture and water, respectively. Linear relationships between the <inline-formula> <tex-math>$S_{mathbf {21}}$ </tex-math></inline-formula> transmission parameter and concentration were found in the selected ranges of 0–50 and 0–35 <inline-formula> <tex-math>$mu $ </tex-math></inline-formula>g/mL for triclosan and glyphosate, respectively. A sensitivity of <inline-formula> <tex-math>$6.87times 10^{-2}$ </tex-math></inline-formula> dB/(<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>g/mL) and a limit of detection (LOD) of <inline-formula> <tex-math>$1.83~mu $ </tex-math></inline-formula>g/mL were obtained with glyphosate solutions. In contrast, the values of <inline-formula> <tex-math>$1.47times 10^{-2}$ </tex-math></inline-formula> dB/(<inline-formula> <tex-math>$mu $ </tex-math></inline-formula>g/mL) and 1.18 <inline-formula> <tex-math>$mu $ </tex-math></inline-formula>g/mL were obtained with triclosan solutions. The results suggest that this technology offers a viable approach for the rapid detection of emerging water contaminants.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-8"},"PeriodicalIF":5.6,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550366","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":"Hierarchical Fault Diagnosis Method for Piezoresistive Pressure Sensor Based on GAF and CNN-SVM","authors":"Yi Ruan;Xinlong Yu;Yigang He;Yuanjie Fang;Jing Wang;Hao Cong","doi":"10.1109/TIM.2025.3582310","DOIUrl":"https://doi.org/10.1109/TIM.2025.3582310","url":null,"abstract":"The requirements for reliability of piezoresistive pressure sensor in modern society are getting higher and higher because of wide application range of sensor and complex working environments. However, due to material properties and working principle (Wheatstone bridge), the failure caused by the degradation of different internal components in piezoresistive pressure sensors exhibits high degree of approximation, mainly reflected in the change in sensor output sensitivity coefficient and signal nonlinear distortion. This brings some challenges to the fault diagnosis of piezoresistive pressure sensor, including how to effectively extract features that can characterize different faults, how to accurately identify faults, and how to judge the severity of corresponding faults. To solve the above crucial problems, a hierarchical fault diagnosis (HFD) method based on Gramian angular fields (GAFs) and convolutional neural networks constructed with support vector machine (CNN-SVM) is presented in this article. First, the piezoresistive pressure sensor fault types are defined according to the functional area. Second, GAF is adopted to encode original output signal of the sensor, which can solve the problem of weak fault features in the original signal. Third, CNN is used to extract the features of Gramian angular summation field (GASF) images transformed by GAF. Moreover, SVM is used as the classifier connected to the flattening layer of CNN. Final, an HFD architecture is proposed to realize fault identification and fault severity judgment. The experimental results indicate the method in this article can effectively extract features, accurately identify different faults of the pressure sensor, and judge the severity of the fault. The accuracy of fault identification achieves 99.34% and the highest accuracy of severity judgment achieves 98%. It proves that the method proposed in this article is applicable and efficient for industrial application.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550576","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":"Suppressing the Static Magnetic Field Error Based on the Rb–Xe Coupling Effect in NMR Angular Velocity Sensor","authors":"Lan Xiao;Jianli Li;Zhanchao Liu;Xuelei Wang;Hao Tian;Yibo Shao","doi":"10.1109/TIM.2025.3579849","DOIUrl":"https://doi.org/10.1109/TIM.2025.3579849","url":null,"abstract":"The nuclear magnetic resonance (NMR) angular velocity sensor exhibits compactness and high precision, leveraging an in situ rubidium (Rb) magnetometer to determine the precession frequencies of double-isotope Xe (¹²⁹Xe and ¹³¹Xe), which are critical for angular velocity determination. However, its performance is substantially affected by static magnetic field variations, which not only alter the precession frequencies of ¹²⁹Xe and ¹³¹Xe but also induce shifts in Rb precession frequency, thereby introducing measurement errors in angular velocity. To mitigate this challenge, this study investigates the impact of static magnetic field shifts on the precession frequency measurement error and presents a suppression method based on the Rb–Xe coupling effect. This method comprehensively evaluates the influence of static magnetic field shifts on the Rb, ¹²⁹Xe, and ¹³¹Xe magnetic moment signals, ultimately proposing a static magnetic field error suppression method. Notably, this approach effectively suppresses the static magnetic field error without influencing the measurement sensitivity. Experimental validation reveals a significant 48.8% reduction in static magnetic field error. Long-term stability of precession frequency measurement error between ¹²⁹Xe and ¹³¹Xe shows a 24.9% reduction in the maximum variation and a 25.1% decrease in bias instability during 2-h continuous monitoring. This innovative approach offers substantial benefits for advancing the performance and reliability of angular velocity sensor.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.6,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144492308","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}