IEEE Transactions on Instrumentation and Measurement最新文献_第4页

Active Autofocusing Method With Plane Tilt Correction for AR Microdisplay Inspection

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-03-27 DOI: 10.1109/TIM.2025.3553246

Chen-Ming Zhong;Yu-Hang Lin;Guo-Long Chen;Li-Hong Zhu;Yi-Jun Lu;Zhong Chen;Wei-Jie Guo

引用次数: 0

Kinetic Chain Analysis of Tennis Stroke Motion Utilizing Wearable Sensors

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-03-26 DOI: 10.1109/TIM.2025.3554882

Weizheng Chen;Yue Jia

{"title":"Kinetic Chain Analysis of Tennis Stroke Motion Utilizing Wearable Sensors","authors":"Weizheng Chen;Yue Jia","doi":"10.1109/TIM.2025.3554882","DOIUrl":"https://doi.org/10.1109/TIM.2025.3554882","url":null,"abstract":"In tennis training, it is crucial to assist players in comprehending their erroneous movements and enhancing motion efficiency. As the foundation of tennis strokes, kinetic chain theory can significantly improve stroke quality and prevent sports injuries. However, current motion analysis predominantly relies on geometric principles, while kinetic analysis methods have yet to effectively translate kinetic chain theory into an accessible and intuitive motion analysis tool. Additionally, a quantitative metric that can directly elucidate and refine the coordination of players’ movements has not been well-established. Consequently, this research integrates digital human motion data with visual graphs and quantifiable features to scientifically analyze tennis strokes. Initially, human motion data is collected using wearable sensors. Subsequently, motion control theory is examined, and hand contribution velocity (HCV) is defined to measure the impact of each human joint on the overall racket-hand velocity. Following this, the tennis stroke is digitally visualized using the innovative kinetic chain diagram (KCD). Next, contribution features and time delay features are extracted from the KCD, further quantifying the characteristics of the motion for assessment purposes. Finally, 15 tennis players across three skill levels participate in a tennis stroke experiment using an automated ball machine, and their stroke motions are visually analyzed using the proposed methods; the effect of motion and grip style on kinetic chain is also discussed. This study presents an innovative approach that integrates sports motion analysis with information technology, thereby facilitating players’ skill enhancement through a scientific methodology.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-8"},"PeriodicalIF":5.6,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817957","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

Contribution Imbalance and the Improvement Method in Multisensor Information Fusion-Based Intelligent Fault Diagnosis of Rotating Machinery

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-03-26 DOI: 10.1109/TIM.2025.3554886

Tantao Lin;Zhijun Ren;Kai Huang;Xinzhuo Zhang;Yongsheng Zhu;Ke Yan;Jun Hong

{"title":"Contribution Imbalance and the Improvement Method in Multisensor Information Fusion-Based Intelligent Fault Diagnosis of Rotating Machinery","authors":"Tantao Lin;Zhijun Ren;Kai Huang;Xinzhuo Zhang;Yongsheng Zhu;Ke Yan;Jun Hong","doi":"10.1109/TIM.2025.3554886","DOIUrl":"https://doi.org/10.1109/TIM.2025.3554886","url":null,"abstract":"The contribution of different signals to rotating machinery fault diagnosis can vary significantly, leading to suboptimal performance in multisensor information fusion-based intelligent fault diagnosis (MIF-IFD). This article examines the issue of imbalanced contributions in MIF-IFD models, explores its causes, and proposes an improvement method. We introduce a contribution discrepancy module to evaluate the contribution of various sensor signals to fault identification. By controlling the training pace of high-contribution branch networks, low-contribution parts are trained sufficiently to keep up. In addition, a distillation module is added to guide each branch network’s learning direction by using outputs from pretrained single-sensor networks as supervisory signals. This approach helps mitigate the degradation in feature extraction ability due to imbalanced training. Experimental results show that the proposed method performs well across two datasets and is valuable for practical deployment in MIF-IFD systems.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-14"},"PeriodicalIF":5.6,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777789","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

Passive Wireless Method for Measuring the Preloads of Threaded Pipe Joints

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-03-26 DOI: 10.1109/TIM.2025.3554896

Chenfei Du;Jianhua Liu;Hao Gong;Jiayu Huang;Xujia Wang

{"title":"Passive Wireless Method for Measuring the Preloads of Threaded Pipe Joints","authors":"Chenfei Du;Jianhua Liu;Hao Gong;Jiayu Huang;Xujia Wang","doi":"10.1109/TIM.2025.3554896","DOIUrl":"https://doi.org/10.1109/TIM.2025.3554896","url":null,"abstract":"Threaded pipe joints are widely used in engineering applications. However, vibration and shock loads during operation can cause preload reduction, potentially leading to issues such as leakage and fatigue failure. Hence, real-time preload monitoring is essential for threaded pipe joints. Generally, to measure preloads, strain gauges can be bonded to the outer surface of the nut, as it is sensitive to slight deformations caused by preload changes in threaded pipe joints. However, traditional strain-gauge-based preload measurement methods require either an embedded battery or an external wired power supply, along with cables for signal transmission, making them unsuitable for confined or inaccessible locations during practical applications. To address these shortcomings, the present study proposes a novel passive wireless method for measuring the preloads of threaded pipe joints. This method includes a power supply, a sensor, and reading modules. It also includes a direct-current filtering technique to mitigate the impact of electromagnetic interference on strain-gauge measurement accuracy. In addition, a temperature compensation model based on multiple linear regression is applied to reduce the sensitivity of preload measurements to temperature. A functional measurement device was subsequently built based on this method. Experimental results validated the greater accuracy and reliability of this method compared to those of other methods, demonstrating its strong potential for engineering applications.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-10"},"PeriodicalIF":5.6,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808993","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

Error-Weighted Collaborative Dictionary Learning for Rolling Bearings Fault Diagnosis

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-03-26 DOI: 10.1109/TIM.2025.3554870

Chuliang Liu;Zhonghe Huang;Xian Wang

引用次数: 0

Design Optimization of a Low-Power and Low-Complexity Digital Driven-Right-Leg Circuit for Active-Electrode Measurement Systems

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-03-26 DOI: 10.1109/TIM.2025.3554913

Techapon Songthawornpong;Woradorn Wattanapanitch

引用次数: 0

An Improved OTDR Signal Denoising Method Based on ICEEMDAN-NLM Algorithm

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-03-26 DOI: 10.1109/TIM.2025.3554855

Fan Zhang;Hao Ran;Bin Li;Xu Zhang;Lei Guo;Xiaoxue Gong

{"title":"An Improved OTDR Signal Denoising Method Based on ICEEMDAN-NLM Algorithm","authors":"Fan Zhang;Hao Ran;Bin Li;Xu Zhang;Lei Guo;Xiaoxue Gong","doi":"10.1109/TIM.2025.3554855","DOIUrl":"https://doi.org/10.1109/TIM.2025.3554855","url":null,"abstract":"Optical time-domain reflectometer (OTDR) is a key device for diagnosing the health of optical fiber links. The backscattered signals it relies on are inevitably affected by noise during transmission, especially when the signal is weak and severely disturbed, making it difficult to identify events in the test curve. Consequently, filtering out noise from these signals to facilitate subsequent fault localization and event recognition has emerged as a critical problem to solve. To address this issue, a novel OTDR signal denoising method has been proposed, combining an improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) with nonlocal means (NLM) filtering. This approach classifies the intrinsic mode function (IMF) components derived from ICEEMDAN based on their sample entropy values, reconstructs the divided signal components, and further filters out high-frequency noise components using the NLM algorithm, to complete the effective denoising of the OTDR signal. Comparative analyses through simulations and practical examples show that this method outperforms traditional denoising techniques such as soft and hard wavelet thresholding, improved wavelet thresholding, NLM, and ICEEMDAN-improved wavelet thresholding in terms of denoising effectiveness. The proposed method has significant improvements/reductions in signal-to-noise ratio (SNR)/root mean square error (RMSE) and provides a new approach for noise reduction in OTDR signals.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-9"},"PeriodicalIF":5.6,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143809022","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

RUL Prediction of Rolling Bearings With MVGA-DSAL: A Multiscale Variant Gaussian Attention Model With Depth-Wise Sparse Attention LSTM

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-03-26 DOI: 10.1109/TIM.2025.3551998

Wei Fan;Zhiyuan Chang;Shaoyi Xu;Zhennan Fan;Ye Yuan

{"title":"RUL Prediction of Rolling Bearings With MVGA-DSAL: A Multiscale Variant Gaussian Attention Model With Depth-Wise Sparse Attention LSTM","authors":"Wei Fan;Zhiyuan Chang;Shaoyi Xu;Zhennan Fan;Ye Yuan","doi":"10.1109/TIM.2025.3551998","DOIUrl":"https://doi.org/10.1109/TIM.2025.3551998","url":null,"abstract":"The performance of mechanical systems relies heavily on the condition of rolling bearings, with any damage potentially leading to catastrophic failures. Accurate bearing health monitoring and remaining useful life (RUL) prediction are crucial. This article proposes a data-driven framework, the multiscale variant Gaussian self-attention (MVGA), integrated with a depth-wise sparse attention long short-term memory (DSAL) mechanism. Specifically, the MVGA module employs a segmented multiscale Gaussian distance mask self-attention mechanism, which prioritizes critical positions and reveals hidden cyclic frequencies, addressing the positional limitations of conventional self-attention methods. The DSAL module, in turn, extracts temporal features across multiple scales, from local to global, facilitating effective multiscale temporal feature processing. Depth-wise propagation minimizes fluctuations in health curves and mitigates information loss and feature confusion from deep stacking, thereby enhancing model stability and robustness. Experimental results demonstrate that our approach effectively addresses limitations within Transformer networks, yielding more accurate predictions and outperforming existing methods in terms of prediction accuracy and model robustness.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.6,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761423","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

Variable-Scale Spectral Feature Gram (VSFgram): A Convergence Property-Driven Autonomous Periodic Transients Extraction Approach

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-03-26 DOI: 10.1109/TIM.2025.3554903

Duxi Shang;Yong Lv;Rui Yuan;Zhuyun Chen;Hewenxuan Li;Ersegun Deniz Gedikli

{"title":"Variable-Scale Spectral Feature Gram (VSFgram): A Convergence Property-Driven Autonomous Periodic Transients Extraction Approach","authors":"Duxi Shang;Yong Lv;Rui Yuan;Zhuyun Chen;Hewenxuan Li;Ersegun Deniz Gedikli","doi":"10.1109/TIM.2025.3554903","DOIUrl":"https://doi.org/10.1109/TIM.2025.3554903","url":null,"abstract":"Precise localization of the optimal frequency band (OFB) and extraction of periodic transients are crucial for effective bearing fault diagnosis. This article proposes a novel approach called the variable-scale spectral feature gram (VSFgram) for periodic transients extraction. The method begins with the development of a spectral feature detector (SFD) based on the convergence property of the variational mode extraction (VME), enabling the identification of the spectral feature. Building on the SFD, a novel multilevel spectral segmentation framework based on variable-scale spectral feature detection (VSFD) is developed, where boundary frequencies (BFs) are adaptively derived from the vibration signal to effectively isolate the OFB. To refine the process, an improved fault feature metric named autonomous frequency domain signal-to-noise ratio (AFDSNR) is proposed, which guides OFB selection and automatically estimates fault characteristic frequency (FCF) from enhanced envelope spectrum (EES) information. The OFB is then located by maximizing the AFDSNR, enabling the reconstruction of corresponding periodic transients. The superiority of the proposed method is validated through simulations and experimental analyses. Compared to methods such as Fast Kurtogram (FK) and Autogram, the VSFgram demonstrates superior accuracy and robustness in bearing fault diagnosis.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-15"},"PeriodicalIF":5.6,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143808995","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

Development of a Broadband Terahertz WR-3 Waveguide Microcalorimeter in the 220~330 GHz

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-03-26 DOI: 10.1109/TIM.2025.3554869

Wenze Yuan;Weidong Hu;Yuming Bai;Xiaohai Cui;Xiaomeng Liu;Jinwen Liu;Yong Li

引用次数: 0