IEEE Transactions on Instrumentation and Measurement最新文献_第9页

Fault Diagnosis Method for Rotating Machinery Based on MSCNN-MGAT 基于MSCNN-MGAT的旋转机械故障诊断方法

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-07-10 DOI: 10.1109/TIM.2025.3587368

Cheng Peng;Hao Li;Weihua Gui;Zhaohui Tang;Xinpan Yuan

引用次数: 0

SiamMTS: Self-Supervised Representation Learning for High-Speed Train Traction System State Prediction 高速列车牵引系统状态预测的自监督表示学习

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-07-10 DOI: 10.1109/TIM.2025.3587364

Zhiqiang Yang;Honghui Dong;Huipeng Zhang;Ruojin Wang

{"title":"SiamMTS: Self-Supervised Representation Learning for High-Speed Train Traction System State Prediction","authors":"Zhiqiang Yang;Honghui Dong;Huipeng Zhang;Ruojin Wang","doi":"10.1109/TIM.2025.3587364","DOIUrl":"https://doi.org/10.1109/TIM.2025.3587364","url":null,"abstract":"Accurate prediction of the high-speed train traction system state ensures safe train operation. Currently, common prediction methods involve dividing the traction system into multiple equipment and establishing separate models based on multisensor signals for each. While effective, this method requires repeated training for different prediction tasks, consuming significant computational resources and limiting the flexibility of model application. Therefore, developing a universal learning framework for predicting the state of various equipment within the traction system is crucial. To this end, this article proposes SiamMTS, a self-supervised representation learning (SSRL) framework based on a Siamese network architecture for multisensor time-series signals. SiamMTS performs self-supervised learning by minimizing the distance between different augmented views of the same sensor sequence in the feature space, thereby extracting a universal time-series representation for improved state prediction performance from the multisensor signals monitoring the traction system. Experimental results demonstrate that SiamMTS performs well when processing datasets from multiple high-speed train traction systems. The encoder obtained during its pretraining phase provides reasonable initialization parameters for downstream tasks, enabling effective prediction of the state of various equipment within the system. Compared with the Supervised model with the same encoder architecture, SiamMTS reduces the average root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) by 22.27%, 23.13%, and 33.21%, respectively, at a prediction step of 10 and by 15.31%, 16.67%, and 20.53%, respectively, at a prediction step of 20. In addition, the total computation time of SiamMTS in predicting the state of four traction system equipment is 41.31% of that required by the Supervised model.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-16"},"PeriodicalIF":5.6,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657353","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

Dual Spatio-Temporal Contrastive Learning Network With Adaptive Threshold Generation for Anomaly Detection of Electric Submersible Pump 电潜泵异常检测的双时空对比学习网络自适应阈值生成

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-07-10 DOI: 10.1109/TIM.2025.3587359

Kang Li;Shuang Li;Qiang Li;Zhikuan Jiao;Jun Fu;Xiaoyong Gao;Laibin Zhang

引用次数: 0

Two 3D-Printed Sensitive Cylindrical Sensors for Characterizing Organic Liquids 两个用于表征有机液体的3d打印灵敏圆柱形传感器

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-07-10 DOI: 10.1109/TIM.2025.3587363

Xiue Bao;Chenhao Yin;Jinkai Li;Li Wang;Dominique Schreurs;Liming Si;Giovanni Crupi;Zhuangzhuang Liu;Houjun Sun

{"title":"Two 3D-Printed Sensitive Cylindrical Sensors for Characterizing Organic Liquids","authors":"Xiue Bao;Chenhao Yin;Jinkai Li;Li Wang;Dominique Schreurs;Liming Si;Giovanni Crupi;Zhuangzhuang Liu;Houjun Sun","doi":"10.1109/TIM.2025.3587363","DOIUrl":"https://doi.org/10.1109/TIM.2025.3587363","url":null,"abstract":"In this article, two highly sensitive sensors based on cylindrical cavities for measuring the complex permittivity of organic liquids are presented. To analyze the sensing performance, the two sensors are designed at the working frequency of around 20 GHz, where the relaxation frequencies of some common lossy liquids are located. For liquid sensing, a Teflon tube is designed at the sensing area. Based on full-wave simulations, the characterization principles are provided, and additionally, the sensing range for the complex permittivity of lossy liquids is analyzed. Next, by using selective laser melting (SLM) additive manufacturing technology, the two sensors are fabricated. However, due to manufacturing tolerance, there is a slight difference between the fabricated sensors and the simulated ones. Therefore, further simulations are performed for calibration of the complex permittivity characterization formulas. The two sensors are used to measure seven pure organic liquids and ten liquid mixtures, which are commonly used for industrial applications. The measurement procedure is simple and nondestructive. By comparing with literature data, the two sensors are validated to provide reliable results. The experimental validation also demonstrates that the proposed devices have good sensitivity to the complex permittivity of liquids. Their good performance is also validated by comparing with other sensors reported in the literature.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-14"},"PeriodicalIF":5.6,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144671254","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

Multiscale Cross-Channel Transformer for Parameter Measurement in Oil–Water Two-Phase Flow 用于油水两相流参数测量的多尺度跨通道变压器

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-07-10 DOI: 10.1109/TIM.2025.3586383

Hanqing Chen;Mengyu Li;Ruiqi Wang;Wei Li;Bang Zhou;Zhiqiang Zhao;Zhongke Gao

{"title":"Multiscale Cross-Channel Transformer for Parameter Measurement in Oil–Water Two-Phase Flow","authors":"Hanqing Chen;Mengyu Li;Ruiqi Wang;Wei Li;Bang Zhou;Zhiqiang Zhao;Zhongke Gao","doi":"10.1109/TIM.2025.3586383","DOIUrl":"https://doi.org/10.1109/TIM.2025.3586383","url":null,"abstract":"The accurate measurement of water cut and total flow rate in oil–water two-phase flow is crucial for effective oilfield management, particularly in high water-cut environments. The inherent complexity of oil–water flows, characterized by diverse flow patterns and significant nonlinear behaviors, poses substantial challenges for traditional measurement techniques. To address these challenges, we propose a multiscale cross-channel transformer (MSCC-Transformer) model designed to analyze high-frequency signals collected by a double-helix microwave sensor (DHMS). The MSCC-Transformer employs cross-channel multiscale embedding (CCME) and multiscale multihead self-attention (MMHSA) mechanisms to capture intricate interchannel dependencies and extract both fine-grained and long-term features. Moreover, a global average pooling (GAP) layer is used to integrate multiscale information, enhancing feature representation and improving measurement accuracy. The experiment determined 1.3 GHz as the optimal operating frequency for the DHMS. Additionally, dynamic experiments show that the MSCC-Transformer significantly outperforms the existing time-series models in measuring water cut and total flow rate, demonstrating its robustness and accuracy.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-10"},"PeriodicalIF":5.6,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634573","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

Characterization of an Automated Permuting Capacitive Device for AC Voltage Ratio Calibration 一种用于交流电压比校准的自动置换电容装置的特性

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-07-10 DOI: 10.1109/TIM.2025.3586355

Seong Su Shin;Wan-Seop Kim;Dan Bee Kim

{"title":"Characterization of an Automated Permuting Capacitive Device for AC Voltage Ratio Calibration","authors":"Seong Su Shin;Wan-Seop Kim;Dan Bee Kim","doi":"10.1109/TIM.2025.3586355","DOIUrl":"https://doi.org/10.1109/TIM.2025.3586355","url":null,"abstract":"A permuting capacitive device (PCD) system has been developed for high-precision calibrations of ac voltage ratio. The PCD consists of 12 capacitors with an adjustable capacitance feature and a coaxial multiplexer for auto-permutation. The PCD capacitors exhibit good stabilities of both short and long terms, and all of their capacitance values lie within <inline-formula> <tex-math>$pm 30times 10^{-6}$ </tex-math></inline-formula>. After a thorough evaluation, the PCD was applied to voltage ratio measurements of inductive voltage dividers (IVDs). The 10:1 voltage ratio value of an IVD, obtained using the PCD at 1 kHz, was in accordance with a reference value to a level of <inline-formula> <tex-math>$10^{-8}$ </tex-math></inline-formula>. The measurement uncertainty was analyzed to be about <inline-formula> <tex-math>$35times 10^{-9}$ </tex-math></inline-formula> (<inline-formula> <tex-math>$k =1$ </tex-math></inline-formula>) at 1 kHz. Reliability of the PCD was further verified across a frequency range from 50 Hz to 20 kHz through 10:1 capacitance ratio measurements using a digital impedance bridge with the calibrated IVD.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-7"},"PeriodicalIF":5.6,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144680862","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

VLF-DETR: Integrating Vision-Language and High-Frequency Features for Transmission Line Defect Detection VLF-DETR：集成视觉语言和高频特征的传输线缺陷检测

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-07-10 DOI: 10.1109/TIM.2025.3586346

Ke Zhang;Jiyuan Yang;Jiacun Wang;Zhaoye Zheng;Xin Sheng;Ningxuan Zhang

{"title":"VLF-DETR: Integrating Vision-Language and High-Frequency Features for Transmission Line Defect Detection","authors":"Ke Zhang;Jiyuan Yang;Jiacun Wang;Zhaoye Zheng;Xin Sheng;Ningxuan Zhang","doi":"10.1109/TIM.2025.3586346","DOIUrl":"https://doi.org/10.1109/TIM.2025.3586346","url":null,"abstract":"Drone-based image capture with deep learning techniques has become a prevalent approach for inspecting transmission lines, allowing for efficient detection of defects and anomalies. However, most existing algorithms rely on the single-modality information, failing to fully exploit the textual modality inherent in labels or the unique characteristics of inspection images, such as sharply focused foregrounds and defocused backgrounds. To overcome these limitations, this article proposes vision-language and high-frequency features DEtection TRansformer (VLF-DETR), a detection model that leverages a multistage training strategy within the Deformable DETR framework. In the first stage, to address the absence of domain-specific knowledge in the general-purpose vision-language model foundational language and vision alignment (FLAVA), we fine-tune FLAVA to learn both textual and visual features relevant to the power industry. In the second stage, to better incorporate textual modality, we introduce conditional queries into Deformable DETR, effectively transferring knowledge from FLAVA into the defect detection model. In the third stage, leveraging the structural characteristics of inspection images, we apply a fast Fourier transform (FFT) to extract high-frequency edge features, suppress background noise, and provide spatial priors. Furthermore, an FFT-based loss function (FFT Loss) is introduced to further ensure the model converges on target regions. Experimental results demonstrate that VLF-DETR significantly outperforms baseline methods, offering a novel and effective solution for transmission line defect detection.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-15"},"PeriodicalIF":5.6,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144705201","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

An Adaptive Enhanced Generalized Integrator-Based Complex Filter for Fundamental Components Extraction Under Weak-Grid Integrated Single-Phase Systems 基于自适应增强广义积分器的单相弱网格综合系统基元提取复杂滤波器

IF 5.9 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-07-10 DOI: 10.1109/TIM.2025.3586360

Faridul Hassan;Amritesh Kumar;Avadh Pati

{"title":"An Adaptive Enhanced Generalized Integrator-Based Complex Filter for Fundamental Components Extraction Under Weak-Grid Integrated Single-Phase Systems","authors":"Faridul Hassan;Amritesh Kumar;Avadh Pati","doi":"10.1109/TIM.2025.3586360","DOIUrl":"https://doi.org/10.1109/TIM.2025.3586360","url":null,"abstract":"Second-order generalized integrator (SOGI)-based phase-locked loop (PLL) and frequency-locked loop (FLL) are widely adopted in various applications such as grid voltage parameter estimation, synchronization, and control of grid-connected converters. However, it is highly sensitive to interharmonics or subharmonics and dc-offset. It causes unequal amplitudes in the quadrature signals and oscillatory errors and ripples in the estimated grid voltage amplitude, phase, and frequency. To address these challenges, this article proposes a frequency adaptive enhanced generalized integrator complex filter (eGICF)-based structure. The proposed method is designed to extract more accurate fundamental in-phase and quadrature-phase signals. It achieves significantly lower harmonics as 0.23% and 0.11%, respectively, even under the highly distorted grid voltage (22.27% THD). The proposed eGICF integrates a GICF with an improved SOGI (ISOGI) serving as an in-loop prefilter. The ISOGI first rejects dc-offsets, high-order harmonics, and producing complex signals. The GICF then refined and effectively rejecting low-order harmonics and interharmonics. The performance of the proposed frequency-adaptive eGICF-based quadrature signal generation (QSG) is evaluated and compared with existing architectures using MATLAB/SIMULINK under highly distorted and dc-offset grid conditions. In addition, the algorithm is implemented on a field-programmable gate array (FPGA)-based controller to validate its effectiveness experimentally under various nonideal grid conditions.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.9,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144725239","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 Semi-Supervised Deep Transfer Learning Method With Improved Extended Isolation Forest for Bearing Fault Diagnosis 基于扩展隔离林的半监督深度迁移学习轴承故障诊断方法

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-07-10 DOI: 10.1109/TIM.2025.3586368

Shaokai Xue;Bing Li;Yingchun Yang;Shuaiqi Zhu;Yuan Yao

{"title":"A Novel Semi-Supervised Deep Transfer Learning Method With Improved Extended Isolation Forest for Bearing Fault Diagnosis","authors":"Shaokai Xue;Bing Li;Yingchun Yang;Shuaiqi Zhu;Yuan Yao","doi":"10.1109/TIM.2025.3586368","DOIUrl":"https://doi.org/10.1109/TIM.2025.3586368","url":null,"abstract":"Industrial condition monitoring leverages transfer learning to enhance equipment diagnostics. However, existing deep transfer learning (DTL) methods face a critical challenge, which still requires substantial annotated samples for robust fault diagnosis, and training deep models remains time-consuming and labor-intensive. To address this gap, we propose a semi-supervised deep transfer method based on an improved extended isolation forest (EIF) for cross-domain bearing fault diagnosis. First, nontarget task samples are scored using an EIF with an arithmetic mean aggregation mechanism for anomaly scores to identify latent fault patterns. A hybrid metric integrating the Kolmogorov–Smirnov (K–S) test and confidence-driven Hellinger distance is employed to generate pseudolabels for anomaly score samples. Subsequently, deep learning (DL) models are trained on these labeled data. Finally, a minimal quantity of target-domain labeled data is used to refine the pretrained models to complete cross-domain bearing fault diagnosis. Extensive validation tests on the HUST bearing dataset and a self-constructed dataset demonstrate that the proposed method achieves high diagnostic accuracy with significantly fewer labeled samples. The experimental results demonstrate that the proposed novel approach with a fine-tuning strategy achieves over 20.52% higher diagnostic accuracy than traditional direct transfer approaches on both the HUST bearing dataset and the self-constructed dataset while reducing the required labeled samples for fine-tuning to only 5%.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144705177","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

Spectral Correlation Analysis for Planetary Gearbox Fault Diagnosis Under Time-Varying Speeds 时变转速下行星齿轮箱故障诊断的谱相关分析

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-07-10 DOI: 10.1109/TIM.2025.3586359

Xiaohui Duan;Zhipeng Feng

{"title":"Spectral Correlation Analysis for Planetary Gearbox Fault Diagnosis Under Time-Varying Speeds","authors":"Xiaohui Duan;Zhipeng Feng","doi":"10.1109/TIM.2025.3586359","DOIUrl":"https://doi.org/10.1109/TIM.2025.3586359","url":null,"abstract":"Planetary gearbox vibration signals feature amplitude modulation (AM) and frequency modulation (FM) and exhibit cyclostationarity. It is essential to identify the signal carrier and modulating frequencies for gear fault diagnosis, but this is very challenging under practical time-varying speed conditions. To address this issue, this article proposes an order–order spectral correlation (OOSC) analysis method, by leveraging the advantage of cyclostationary (CS) signal analysis in modulation feature extraction. First, the nonstationary signal under time-varying speeds is resampled in angular domain, making it order-stable relative to the rotating frequency. Then, the discrete random separation (DRS) is applied to the resampled signal, to cancel interferences due to rotating frequency irrelevant components. Finally, the separated resampled signal is analyzed by spectral correlation (SC) in order–order domain. This proposed method generalizes CS analysis from constant speed to time-varying speed conditions and can reveal both the signal carrier and modulating frequency orders simultaneously, thus facilitating gear fault diagnosis under practical nonstationary conditions. It is experimentally validated on a wind turbine drivetrain test rig, and the sun gear faults in both planetary stages are successfully diagnosed.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-15"},"PeriodicalIF":5.6,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144687808","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