IEEE Transactions on Instrumentation and Measurement最新文献

{"title":"ECGAN: An Efficient Diagnostic Strategy for Hidden Deterioration in DC-DC Converters","authors":"Li Wang;Yuanpeng Ma;Chenhao Wu;Feng Lyu;Liang Hua","doi":"10.1109/TIM.2025.3551485","DOIUrl":"https://doi.org/10.1109/TIM.2025.3551485","url":null,"abstract":"This article highlights the critical role of reliable dc-dc converter operation in ensuring the stability of power conversion systems, especially in extreme environments like underwater observation networks. Addressing the common challenge in dc-dc converter fault diagnosis—overlooking the periodic characteristics of electrical signals during feature extraction from large datasets—we propose an innovative diagnostic method that combines adaptive wavelet transform (AWT) and an enhanced classifier generative adversarial network (ECGAN). First, AWT dynamically selects and optimizes WBFs to accurately capture degradation features in the output voltage signals. Then, a compound rainbow convolution block (CRConvBlock) is used to enhance the time-frequency representation of these signals, integrating temporal and frequency information for improved feature extraction. Furthermore, the proposed ECGAN model is guided by an adaptive loss optimization framework (ALOF) that dynamically adjusts training weights to balance sample quality and classification accuracy. Experimental validation in four different circuits demonstrates the high accuracy and robustness of the method, highlighting its potential for practical engineering applications.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-9"},"PeriodicalIF":5.6,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735415","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":"Wide-Range Ocean Current Speed Estimation From Buoy Measurement Data Using Machine Learning","authors":"Biswajit Haldar;Boby George;Arul Muthiah Manickavasagam;Atmanand Malayath Aravindakshan","doi":"10.1109/TIM.2025.3552383","DOIUrl":"https://doi.org/10.1109/TIM.2025.3552383","url":null,"abstract":"The conventional Doppler-based single point current meters (SPCM) which are used for the measurement of surface ocean current speed and direction in the moored data buoy systems face challenges as their output is susceptible to biofouling. SPCM requires noticeable power for the operation and it is expensive. A recently reported innovative approach which involves integrating load cell in the mooring line of a data buoy, is a viable option for ocean current measurement with advantages such as lower power requirements, less cost, and resistance to biofouling. However, the reported method is useful only during extreme weather conditions when the mooring line is sufficiently stretched. In this article, an effort is made to overcome this limitation, by incorporating machine-learning (ML) techniques with the additional measurement data, such as wind, wave, and buoy position along with the mooring load. The new approach was developed and tested for two data buoys deployed in the Arabian Sea and the Bay of Bengal over nearly a year duration. This study compares six different ML models, ultimately identifying random forest (RF) as the top-performing model with a correlation value of 0.92 between the observed and estimated current for both the buoys and the root mean square error (RMSE) of 0.072 and 0.042 m/s for BD08 and AD07 buoy in the Bay of Bengal and Arabian Sea, respectively. The study shows that the proposed method is capable of estimating a wide range of ocean currents reliably with very good accuracy.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-10"},"PeriodicalIF":5.6,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777957","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 Novel Dual-Domain Adversarial Method for Vibration Signal Denoising in Bearing Fault Diagnosis","authors":"Guangjie Han;Junhao Shen;Zhen Wang;Yuanyang Zhu;Yuhang Xie","doi":"10.1109/TIM.2025.3551836","DOIUrl":"https://doi.org/10.1109/TIM.2025.3551836","url":null,"abstract":"Rolling bearings are critical components in rotating machinery, and their malfunction can result in significant losses. Therefore, accurate fault diagnosis is essential for maintaining the performance and reliability of such machinery. However, this process is often hindered by the substantial noise in industrial environments, which can contaminate bearing vibration data and obscure critical features. To address the challenge of recovering vibration signals contaminated by noise, we propose a deep learning (DL) framework named dual-domain adversarial residual encoder-decoder networks (DAREDNs). This framework utilizes a cascade structure composed of adversarial residual encoder-decoder networks (AREDNs) for denoising. The first AREDN processes noisy signals in the time domain to generate initial denoised signals, while the second AREDN refines the signals further in the frequency domain. Additionally, we introduce dual-domain reconstruction and adversarial regularization strategies during training to enhance the feature extraction capabilities of the network. To evaluate its performance, we design targeted experimental strategies and compare DAREDN with three other DL-based denoising models using both public and private bearing fault datasets. Experimental results demonstrate that DAREDN effectively reconstructs vibration signals under high noise conditions, significantly improving fault diagnosis accuracy. These findings highlight the potential of DAREDN as a robust solution for noise-robust fault diagnosis in industrial applications.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761513","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":"Fusion of Measurement and Simulation Technique for Electromagnetic Environment Analysis in Large-Scale Urban Areas","authors":"Haoxuan Gong;Xing Wang;Chunheng Liu;Yanan Zhao;Ying Liu;Ning Li;Wenli Yang","doi":"10.1109/TIM.2025.3552385","DOIUrl":"https://doi.org/10.1109/TIM.2025.3552385","url":null,"abstract":"Accurate analysis of electromagnetic environments, especially in large-scale urban areas, is essential to ensure the safety of urban electromagnetic spaces. This article presents a novel fusion technique that combines measurements from spectrum sensing sensors with an efficient hybrid algorithm that integrates parallel computation, higher order method of moments (HOMoM), and uniform geometrical theory of diffraction (UTD), to improve the accuracy of electromagnetic environmental analyses. The proposed technique addresses key challenges, including high costs associated with extensive measurements and poor accuracy in results fit from limited measurement data, as well as slow and imprecise simulation outcomes. By continuously updating the simulated electromagnetic field data with measured data, the technique improves simulation accuracy and enables efficient analysis of large urban areas. Experimental results from a 60-km2 area in Xi’an demonstrated a root mean square error (RMSE) of less than 8 dB, with updates processed in under 8 s, making it a practical and efficient solution for large-scale urban electromagnetic environment analysis.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761461","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":"PSO-Based Optimal Coverage Path Planning for Surface Defect Inspection of 3C Components With a Robotic Line Scanner","authors":"Hongpeng Chen;Shengzeng Huo;Muhammad Muddassir;Hoi-Yin Lee;Yuli Liu;Junxi Li;Anqing Duan;Pai Zheng;David Navarro-Alarcon","doi":"10.1109/TIM.2025.3552466","DOIUrl":"https://doi.org/10.1109/TIM.2025.3552466","url":null,"abstract":"The automatic inspection of surface defects is an essential task for quality control in the computers, communications, and consumer (3C) electronics industry. Traditional inspection mechanisms (i.e., line-scan sensors) have a limited field of view (FOV), thus prompting the necessity for a multifaceted robotic inspection system capable of comprehensive scanning. Optimally selecting the robot’s viewpoints and planning a path is regarded as coverage path planning (CPP), a problem that enables inspecting the object’s complete surface while reducing the scanning time and avoiding misdetection of defects. In this article, we present a new approach for robotic line scanners to detect surface defects of 3C free-form objects automatically. A two-stage region segmentation method defines the local scanning based on the random sample consensus (RANSAC) and K-means clustering to improve the inspection coverage. The proposed method also consists of an adaptive region-of-interest (ROI) algorithm to define the local scanning paths. Besides, a particle swarm optimization (PSO)-based method is used for global inspection path generation to minimize the inspection time. The developed method is validated by simulation-based and experimental studies on various free-form workpieces, and its performance is compared with that of two state-of-the-art solutions. The reported results demonstrate the feasibility and effectiveness of our proposed method.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761416","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 Flexible Electrical Capacitance Tomography Sensor for Proximity Object Recognition","authors":"Xiaoli Xu;Xudong Guo;Wendong Zheng;Huaping Liu","doi":"10.1109/TIM.2025.3551985","DOIUrl":"https://doi.org/10.1109/TIM.2025.3551985","url":null,"abstract":"The ability to recognize objects plays a crucial role in the application of robots. Electrical capacitance tomography (ECT) effectively addresses the limitations of other types of proximity sensors in object recognition. This study introduces a flexible ECT sensor, examines the characteristics of coplanar ECT sensors in measurement strategies, and utilizes Tikhonov regularization for position and target contour reconstruction. A detailed analysis of the sensor’s response and performance metrics is conducted. Under proximity conditions, the support vector machine (SVM) achieves a recognition accuracy of up to 93.1% for 15 different object items. To validate the practicality and effectiveness of the sensor, we design a simple physical experiment that combines close-range identification based on the ECT sensor with obstacle avoidance using a robotic arm. The experimental results indicate that the robotic arm can select appropriate avoidance strategies based on the potential impact of the object on itself, thereby enhancing obstacle avoidance efficiency. This indicates that through ECT sensors, effective proximity recognition of objects in the environment is achievable, offering a new solution to enhance the perceptual abilities and autonomy of robots in complex environments.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761469","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 Pre-Processing Signal Modeling Approach for Multichannel ATI Digitizers","authors":"Sitao Mei;Yijiu Zhao;E. Shao;Yusen Wang;Yi Zhang","doi":"10.1109/TIM.2025.3552441","DOIUrl":"https://doi.org/10.1109/TIM.2025.3552441","url":null,"abstract":"Asynchronous time-interleaved (ATI) analog-to-digital converter (ADC) architecture utilizes pre-processing, low-pass filtering, and ADCs to simultaneously enhance the sampling rate and bandwidth of the acquisition system. We propose a novel approach to generate pre-processing signal in multichannel ATI architecture. The generated pre-processing signal is no longer limited to pulse wave or square wave with a fixed duty. We derive the condition for reconstructing input signal as the invertibility of the pre-processing matrix. Additionally, we analyzed the error sources in the multichannel ATI. Based on the spectral shift characteristics of the ATI, we propose an error coefficient estimation method. Using the estimated error coefficient, a digital filter can be designed to calibrate the system. A four-channel hardware prototype was developed to evaluate the performance of the proposed method, satisfactory results were obtained and the feasibility of the proposed approach was demonstrated.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-9"},"PeriodicalIF":5.6,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143769486","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":"EEG Rhythm-Based Functional Brain Connectivity for Automated Detection of Schizophrenia Employing Deep Learning","authors":"Sudip Modak;Kaniska Samanta;Suman Halder;Soumya Chatterjee","doi":"10.1109/TIM.2025.3552448","DOIUrl":"https://doi.org/10.1109/TIM.2025.3552448","url":null,"abstract":"In the present contribution, a novel framework for automated detection of healthy and schizophrenic (SCZ) electroencephalogram (EEG) signals is proposed employing multiplex weighted visibility graph (MWVG)-aided functional brain connectivity analysis and deep residual network (ResNet). For this purpose, EEG signals recorded from different regions of the brain using multichannel EEG system, have been channel-wise decomposed into different frequency bands known as brain rhythms. Following this, for each rhythm, a novel approach for construction of functional brain connectivity for both healthy and SCZ patients is proposed using inter-layer similarity of nodal local efficiency (LE) measures. The red-green-blue (RGB) images of rhythm-wise brain connectivity patterns obtained for healthy and SCZ patients were finally fed to a 19-layer customized lightweight ResNet model for automated feature extraction and classification purpose. It was observed that the brain connectivity patterns for each brain rhythm showed significant alterations between healthy and SCZ patients. Further, it was also observed that for the alpha brain rhythm, distinct difference is perceived, which yielded highest detection accuracy of 98.72% and 99.93%, respectively for two publicly available benchmark datasets.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-9"},"PeriodicalIF":5.6,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777955","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 Multiscale Cross-Channel Attention Network for Remaining Useful Life Prediction With Variable Sensors","authors":"Tianao Zhang;Li Jiang;Ruyi Huang;Xin Zhang","doi":"10.1109/TIM.2025.3551011","DOIUrl":"https://doi.org/10.1109/TIM.2025.3551011","url":null,"abstract":"The remaining useful life (RUL) prediction of machinery based on deep learning (DL) represents a crucial component in the field of prognostics and health management (PHM). However, these DL-based methods for RUL prediction tend to become unreliable during online inference when only partial signals are available. To address this issue, we introduce the variable sensors scenario and propose a multiscale cross-channel attention network (MSCCAN) specifically designed for RUL prediction with variable sensors. For each input sample with variable sensors, the embedding layer is utilized to transform the dimensions of the input tensor. The multiscale cross-channel attention (MSCCA) layer is employed to extract and fuse multichannel degradation information, where the multiscale convolutional attention (MSCA) blocks extract the multiscale degradation features, and the anti-missing cross-channel attention (AMCCA) block effectively integrates feature information while mitigating interference from missing sensors. A mask global average (MGA) layer is used to compress high-dimensional features without being affected by the channels from missing sensors. Moreover, a new data augmentation method is used to improve the robustness of the model to the inputs with variable sensors. Finally, the experiments on the commercial modular aero-propulsion system simulation (CMAPSS) dataset and the New CMAPSS (N-CMAPSS) dataset validate the effectiveness of MSCCAN under both normal and variable sensor scenarios. Experimental results demonstrate that the proposed method can reduce the prediction error by more than 40% compared with the comparison methods under the worst scenario with variable-sensor inputs.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-10"},"PeriodicalIF":5.6,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143716512","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":"MIMO Microwave Sensor for Selective and Simultaneous Detection of Methanol and Ethanol Gases at Room Temperature","authors":"Mohammad Mahmudul Hasan;Todd Cowen;Onur Alev;Michael Cheffena","doi":"10.1109/TIM.2025.3551791","DOIUrl":"https://doi.org/10.1109/TIM.2025.3551791","url":null,"abstract":"This article presents a machine learning (ML)-assisted novel microwave sensor for the simultaneous and selective detection of multiple volatile organic compounds (VOCs) at room temperature (RT) using molecularly imprinted polymer (MIP)/carbon nanotube (CNT)-based sensing layers. The synthesized materials were characterized using Fourier-transform infrared (FT-IR) spectroscopy and subsequently used to develop two chemiresistive sensors, two single-port antenna sensors, and a two-port multiple-input-multiple-output (MIMO) antenna sensor, all systematically developed step by step. First, high-precision interdigitated electrode (IDE)-based sensors were functionalized with MIP/CNT sensing materials for individual target VOCs. These sensors were then evaluated for their electrical and gas-sensing properties and optimized to achieve the desired sensitivity. Next, antenna sensors were developed using these optimized structures, demonstrating selectivity for methanol and ethanol with a sensitivity of ~1.0 MHz/1000 ppm. The detection limits (DLs) were 77 ppm for ethanol and 90 ppm for methanol, both well below their safety thresholds, ensuring the sensors’ suitability for practical applications. ML-based signal processing techniques were used to isolate cross-reactivity between chemical interferents and mutual coupling effects from closely spaced array elements. This enabled the simultaneous and selective detection of multiple VOCs in complex mixtures. The ML models trained on experimental data achieved an impressive F1 score of 0.9917, demonstrating accurate discrimination of VOC types. In addition, the models produced an R-squared value of 0.994 for gas concentration estimation, confirming their predictive accuracy. The developed sensors exhibited high selectivity and specificity when tested against methanol, ethanol, acetone, and isopropanol. Moreover, the antenna sensors operated within their bandwidth during gas sensing, eliminating the need for complex tuning circuits. In addition, perturbation analysis confirmed the ML model’s robustness, as it maintained high accuracy despite input noise, ensuring reliable real-world performance.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.6,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740366","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}