Recently, long-term gas detection has attracted much attention due to its being a key factor for electronic nose (E-Nose) applications. However, the sensor drift effect can significantly reduce the performance of the sensor. Therefore, in this work, we proposed a new drift compensation method by optimizing feature selection, model construction, and training methods to study drift-resistant gas detection based on convolutional neural network (CNN) methods. First, the attention mechanism is used to screen the specific features of the gas data and remove the low-weight features. Moreover, a multiscale feature extraction network is designed so that the features fused by the three-layer convolution are used as the final classification feature input to extract the depth features keeping the drift unchanged. Simultaneously, the segmented training method and the targeted cyclic training model are adopted to reduce the required experimental data. Importantly, based on the largest gas drift dataset currently, the proposed method maintains the average gas detection accuracy beyond 80% in three years, and the long-term stability of gas detection is effectively improved. Therefore, our findings provide an effective way to solve the sensor drift effect.
{"title":"Anti-Drift Gas Detection Algorithm Based on Neural Network","authors":"Jiayi Guo;Xu Li;Xiulei Li;Zheng Liang;Juexian Cao;Xiaolin Wei","doi":"10.1109/TIM.2024.3488159","DOIUrl":"https://doi.org/10.1109/TIM.2024.3488159","url":null,"abstract":"Recently, long-term gas detection has attracted much attention due to its being a key factor for electronic nose (E-Nose) applications. However, the sensor drift effect can significantly reduce the performance of the sensor. Therefore, in this work, we proposed a new drift compensation method by optimizing feature selection, model construction, and training methods to study drift-resistant gas detection based on convolutional neural network (CNN) methods. First, the attention mechanism is used to screen the specific features of the gas data and remove the low-weight features. Moreover, a multiscale feature extraction network is designed so that the features fused by the three-layer convolution are used as the final classification feature input to extract the depth features keeping the drift unchanged. Simultaneously, the segmented training method and the targeted cyclic training model are adopted to reduce the required experimental data. Importantly, based on the largest gas drift dataset currently, the proposed method maintains the average gas detection accuracy beyond 80% in three years, and the long-term stability of gas detection is effectively improved. Therefore, our findings provide an effective way to solve the sensor drift effect.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-8"},"PeriodicalIF":5.6,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636491","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}
Pub Date : 2024-11-07DOI: 10.1109/TIM.2024.3488158
Xiang Liu
Defect detection is a critical component of quality control in the manufacturing of printed circuit boards (PCBs). However, accurately detecting PCB defects is challenging because they are very small and inconspicuous. In this article, an adaptive defect-aware attention network (ADANet) is proposed for PCB defect detection, and it contains two main modules: small defect preserving and location (SDPL) and defect segmentation prediction (DSP), where the SDPL module is designed to extract the high-resolution and multiscale defect feature representations to avoid the loss of small defects caused by model depth and then locate their positions with a deformable Transformer, and the DSP module is developed to predict their categories and masks. Experimental results conducted on two PCB datasets show that the proposed ADANet can surpass state-of-the-art approaches and achieve high performance in multiscale defect classification and detection results.
{"title":"An Adaptive Defect-Aware Attention Network for Accurate PCB-Defect Detection","authors":"Xiang Liu","doi":"10.1109/TIM.2024.3488158","DOIUrl":"https://doi.org/10.1109/TIM.2024.3488158","url":null,"abstract":"Defect detection is a critical component of quality control in the manufacturing of printed circuit boards (PCBs). However, accurately detecting PCB defects is challenging because they are very small and inconspicuous. In this article, an adaptive defect-aware attention network (ADANet) is proposed for PCB defect detection, and it contains two main modules: small defect preserving and location (SDPL) and defect segmentation prediction (DSP), where the SDPL module is designed to extract the high-resolution and multiscale defect feature representations to avoid the loss of small defects caused by model depth and then locate their positions with a deformable Transformer, and the DSP module is developed to predict their categories and masks. Experimental results conducted on two PCB datasets show that the proposed ADANet can surpass state-of-the-art approaches and achieve high performance in multiscale defect classification and detection results.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-11"},"PeriodicalIF":5.6,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672062","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}
The detection of defect severity in cable terminations plays a critical role in ensuring the safe and stable operation of high-speed trains (HSTs). However, the partial discharge (PD) characteristics of the same type of defect can appear similar across different severities, posing challenges for accurate insulation defect degree identification. Consequently, this article proposes an image transformation method, named the equivalent bandwidth matrix of relative locations (EBMRLs), coupled with the self-guided transformer (SG-Former) algorithm, which is more effective for fine-grained image recognition, to accurately identify different degrees of defects with similar PD characteristics. In the proposed approach, the original PD signals are first converted into images using EBMRL. This transformation embeds the characteristic and bandwidth information from the original PD data into the images, thereby reducing the similarity of information between classes in the transformed images and enhancing their distinguishability. Subsequently, the local and global features of the transformed EBMRL images are extracted to train the SG-Former model. The model is finally utilized to identify the severity of defects in cable terminations. The results demonstrate that the method proposed in this article achieves better performance compared with some of the state-of-the-art methods.
{"title":"Equivalent Bandwidth Matrix of Relative Locations: Image Modeling Method for Defect Degree Identification of In-Vehicle Cable Termination","authors":"Kai Liu;Shibo Jiao;Guangbo Nie;Hui Ma;Bo Gao;Chuanming Sun;Dongli Xin;Tapan Kumar Saha;Guangning Wu","doi":"10.1109/TIM.2024.3481567","DOIUrl":"https://doi.org/10.1109/TIM.2024.3481567","url":null,"abstract":"The detection of defect severity in cable terminations plays a critical role in ensuring the safe and stable operation of high-speed trains (HSTs). However, the partial discharge (PD) characteristics of the same type of defect can appear similar across different severities, posing challenges for accurate insulation defect degree identification. Consequently, this article proposes an image transformation method, named the equivalent bandwidth matrix of relative locations (EBMRLs), coupled with the self-guided transformer (SG-Former) algorithm, which is more effective for fine-grained image recognition, to accurately identify different degrees of defects with similar PD characteristics. In the proposed approach, the original PD signals are first converted into images using EBMRL. This transformation embeds the characteristic and bandwidth information from the original PD data into the images, thereby reducing the similarity of information between classes in the transformed images and enhancing their distinguishability. Subsequently, the local and global features of the transformed EBMRL images are extracted to train the SG-Former model. The model is finally utilized to identify the severity of defects in cable terminations. The results demonstrate that the method proposed in this article achieves better performance compared with some of the state-of-the-art methods.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-10"},"PeriodicalIF":5.6,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598615","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}
Pub Date : 2024-11-05DOI: 10.1109/TIM.2024.3485406
Min Zhang;Jiamin Li;Jiliang Mo;Mingxue Shen;Zaiyu Xiang;Zhongrong Zhou
The braking system of high-speed trains is directly related to the operation safety of the train. The brake pads, which play a crucial role, will inevitably undergo uneven wear in long-term use, posing safety hazards to train braking. As the trains are in normal operating condition for long periods, it is difficult to collect usable uneven wear data, and there is a situation of data imbalance. This article proposes a trade-off contrastive learning network (TCLN), utilizing the differences between data and balancing the weights of different classes, which can realize the condition monitoring under the data imbalance of brake pads. First, data augmentation is employed to provide sufficient and diverse data for contrastive learning, and nonlinear features are extracted by a quadratic convolutional neural network (QCNN). Then, the designed class-weighted method is utilized to improve the characterization ability of the minority class data and realize the equidistant representation of features for each class, which in turn achieves the purpose of paying equal attention to all classes. Finally, the effectiveness of the proposed method is verified using the dataset collected from the scaling experiments, and the results show that the proposed method has higher accuracy and efficiency compared to other methods, which can still accurately identify the brake pad condition when the data are highly imbalanced.
{"title":"High-Speed Train Brake Pads Condition Monitoring Based on Trade-Off Contrastive Learning Network","authors":"Min Zhang;Jiamin Li;Jiliang Mo;Mingxue Shen;Zaiyu Xiang;Zhongrong Zhou","doi":"10.1109/TIM.2024.3485406","DOIUrl":"https://doi.org/10.1109/TIM.2024.3485406","url":null,"abstract":"The braking system of high-speed trains is directly related to the operation safety of the train. The brake pads, which play a crucial role, will inevitably undergo uneven wear in long-term use, posing safety hazards to train braking. As the trains are in normal operating condition for long periods, it is difficult to collect usable uneven wear data, and there is a situation of data imbalance. This article proposes a trade-off contrastive learning network (TCLN), utilizing the differences between data and balancing the weights of different classes, which can realize the condition monitoring under the data imbalance of brake pads. First, data augmentation is employed to provide sufficient and diverse data for contrastive learning, and nonlinear features are extracted by a quadratic convolutional neural network (QCNN). Then, the designed class-weighted method is utilized to improve the characterization ability of the minority class data and realize the equidistant representation of features for each class, which in turn achieves the purpose of paying equal attention to all classes. Finally, the effectiveness of the proposed method is verified using the dataset collected from the scaling experiments, and the results show that the proposed method has higher accuracy and efficiency compared to other methods, which can still accurately identify the brake pad condition when the data are highly imbalanced.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-10"},"PeriodicalIF":5.6,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636293","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}
The effective representation and feature extraction of 3-D scenes from sparse and unstructured point clouds pose a significant challenge in 3-D object detection. In this article, we propose TransMRE, a network that enables fully sparse multiple observation plane feature fusion using LiDAR point clouds as single-modal input. TransMRE achieves this by sparsely factorizing a 3-D voxel scene into three separate observation planes: XY, XZ, and YZ planes. In addition, we propose Observation Plane Sparse Fusion and Interaction to explore the internal relationship between different observation planes. The Transformer mechanism is employed to realize feature attention within a single observation plane and feature attention across multiple observation planes. This recursive application of attention is done during multiple observation plane projection feature aggregation to effectively model the entire 3-D scene. This approach addresses the limitation of insufficient feature representation ability under a single bird’s-eye view (BEV) constructed by extremely sparse point clouds. Furthermore, TransMRE maintains the full sparsity property of the entire network, eliminating the need to convert sparse feature maps into dense feature maps. As a result, it can be effectively applied to LiDAR point cloud data with large scanning ranges, such as Argoverse 2, while ensuring low computational complexity. Extensive experiments were conducted to evaluate the effectiveness of TransMRE, achieving 64.9 mAP and 70.4 NDS on the nuScenes detection benchmark, and 32.3 mAP on the Argoverse 2 detection benchmark. These results demonstrate that our method outperforms state-of-the-art methods.
{"title":"TransMRE: Multiple Observation Planes Representation Encoding With Fully Sparse Voxel Transformers for 3-D Object Detection","authors":"Ziming Zhu;Yu Zhu;Kezhi Zhang;Hangyu Li;Xiaofeng Ling","doi":"10.1109/TIM.2024.3480206","DOIUrl":"https://doi.org/10.1109/TIM.2024.3480206","url":null,"abstract":"The effective representation and feature extraction of 3-D scenes from sparse and unstructured point clouds pose a significant challenge in 3-D object detection. In this article, we propose TransMRE, a network that enables fully sparse multiple observation plane feature fusion using LiDAR point clouds as single-modal input. TransMRE achieves this by sparsely factorizing a 3-D voxel scene into three separate observation planes: XY, XZ, and YZ planes. In addition, we propose Observation Plane Sparse Fusion and Interaction to explore the internal relationship between different observation planes. The Transformer mechanism is employed to realize feature attention within a single observation plane and feature attention across multiple observation planes. This recursive application of attention is done during multiple observation plane projection feature aggregation to effectively model the entire 3-D scene. This approach addresses the limitation of insufficient feature representation ability under a single bird’s-eye view (BEV) constructed by extremely sparse point clouds. Furthermore, TransMRE maintains the full sparsity property of the entire network, eliminating the need to convert sparse feature maps into dense feature maps. As a result, it can be effectively applied to LiDAR point cloud data with large scanning ranges, such as Argoverse 2, while ensuring low computational complexity. Extensive experiments were conducted to evaluate the effectiveness of TransMRE, achieving 64.9 mAP and 70.4 NDS on the nuScenes detection benchmark, and 32.3 mAP on the Argoverse 2 detection benchmark. These results demonstrate that our method outperforms state-of-the-art methods.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-13"},"PeriodicalIF":5.6,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636362","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}
Pub Date : 2024-11-04DOI: 10.1109/TIM.2024.3488141
Tangwen Yin;Hongtian Chen;Dan Huang;Hesheng Wang
Causality is an active relationship that transforms possibility into actuality, underscoring the limitation of relying on averages to address rare events. This study proposes a counterfactual covariate causal discovery mechanism on nonlinear extremal quantiles (CCCD-NEQs) to impute potential outcomes, measure unobservable causalities, and unveil hidden causal relationships in safety-critical systems. We created a multilevel statistical model called mixed-effect and causal-covariate statistical model with dynamic quantiles (MCSM-DQs), which incorporates mixed effects, causal covariates, and dynamic quantiles. Leveraging the exponential family distribution over MCSM-DQ ensures simplified parameter estimation and enhanced computation efficiency, enabling the bootstrapping prediction of counterfactual outcomes at dynamic quantiles to reveal causal relationships and mitigate confounding effects. We applied the CCCD-NEQ approach to identify the potential causal effects among aircraft configuration, decision-making capabilities, and flight safety. Results revealed previously unknown causal relationships concerning rare safety incidents that cannot be detected using conventional instrumental analytics. Our new counterfactual causal discovery mechanism provides opportunities to uncover hidden causality on nonlinear extremal quantiles, highlighting the forward design and optimization of systems for adaptability, robustness, intelligence, and safety.
{"title":"Counterfactual Covariate Causal Discovery on Nonlinear Extremal Quantiles","authors":"Tangwen Yin;Hongtian Chen;Dan Huang;Hesheng Wang","doi":"10.1109/TIM.2024.3488141","DOIUrl":"https://doi.org/10.1109/TIM.2024.3488141","url":null,"abstract":"Causality is an active relationship that transforms possibility into actuality, underscoring the limitation of relying on averages to address rare events. This study proposes a counterfactual covariate causal discovery mechanism on nonlinear extremal quantiles (CCCD-NEQs) to impute potential outcomes, measure unobservable causalities, and unveil hidden causal relationships in safety-critical systems. We created a multilevel statistical model called mixed-effect and causal-covariate statistical model with dynamic quantiles (MCSM-DQs), which incorporates mixed effects, causal covariates, and dynamic quantiles. Leveraging the exponential family distribution over MCSM-DQ ensures simplified parameter estimation and enhanced computation efficiency, enabling the bootstrapping prediction of counterfactual outcomes at dynamic quantiles to reveal causal relationships and mitigate confounding effects. We applied the CCCD-NEQ approach to identify the potential causal effects among aircraft configuration, decision-making capabilities, and flight safety. Results revealed previously unknown causal relationships concerning rare safety incidents that cannot be detected using conventional instrumental analytics. Our new counterfactual causal discovery mechanism provides opportunities to uncover hidden causality on nonlinear extremal quantiles, highlighting the forward design and optimization of systems for adaptability, robustness, intelligence, and safety.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636329","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}
Pub Date : 2024-11-04DOI: 10.1109/TIM.2024.3485462
Ze Chen;Yuan Zhang;Zifei Cao;Yongmeng Liu
As the “heart” of the aviation industry, high-performance aero-engines have always been a stumbling block restricting rapid development. Curvic couplings are widely used in the assembly of multistage aero-engine rotors. The coaxiality of the assembly significantly influences the performance and life of the aero-engine, so it is necessary to predict and optimize the assembly coaxiality. Aiming at three key problems, we propose an assembly coaxiality optimization and prediction approach. In this approach, we measure 3-D point clouds by a line-structured light array scanning measurement system and come up with a weighted iterative closest point (ICP) algorithm to perform a virtual assembly of the point cloud model to regulate the assembly precision. Ultimately, rotors with curvic couplings are used to experimentally validate the coaxiality prediction and optimization approach. According to the experimental findings, the two-/ three-stage rotors assemblies’ maximum coaxiality prediction errors under eight distinct assembly phases are 4.8 and $7.7~mu $ m, respectively. The two-/three-stage rotors optimization assemblies’ coaxiality errors are decreased by 11.9 and $31.8~mu $ m, respectively, compared with the direct assembly without optimization. The three-stage rotors’ assembly accuracy is improved by 12.09%. The results show the effectiveness of the proposed method.
{"title":"Precision Regulation in Multistage Aero-Engine Rotors With Curvic Couplings Using Line-Structured Light Array Scanning and Virtual Assembly","authors":"Ze Chen;Yuan Zhang;Zifei Cao;Yongmeng Liu","doi":"10.1109/TIM.2024.3485462","DOIUrl":"https://doi.org/10.1109/TIM.2024.3485462","url":null,"abstract":"As the “heart” of the aviation industry, high-performance aero-engines have always been a stumbling block restricting rapid development. Curvic couplings are widely used in the assembly of multistage aero-engine rotors. The coaxiality of the assembly significantly influences the performance and life of the aero-engine, so it is necessary to predict and optimize the assembly coaxiality. Aiming at three key problems, we propose an assembly coaxiality optimization and prediction approach. In this approach, we measure 3-D point clouds by a line-structured light array scanning measurement system and come up with a weighted iterative closest point (ICP) algorithm to perform a virtual assembly of the point cloud model to regulate the assembly precision. Ultimately, rotors with curvic couplings are used to experimentally validate the coaxiality prediction and optimization approach. According to the experimental findings, the two-/ three-stage rotors assemblies’ maximum coaxiality prediction errors under eight distinct assembly phases are 4.8 and \u0000<inline-formula> <tex-math>$7.7~mu $ </tex-math></inline-formula>\u0000m, respectively. The two-/three-stage rotors optimization assemblies’ coaxiality errors are decreased by 11.9 and \u0000<inline-formula> <tex-math>$31.8~mu $ </tex-math></inline-formula>\u0000m, respectively, compared with the direct assembly without optimization. The three-stage rotors’ assembly accuracy is improved by 12.09%. The results show the effectiveness of the proposed method.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-11"},"PeriodicalIF":5.6,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636361","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}
An easy-to-fabricate, full circle range (0°–360°), planar coil-based variable reluctance (VR) angle transducer with enhanced linearity is presented in this article. The proposed sensor system aims to mitigate the limitations of the existing VR angle sensors, particularly their limited accuracy and nonlinearity, resulting from the inherent sensor output characteristics. By carefully designing the coil geometry to achieve uniform flux distribution and implementing a simple semicircular-shaped rotor, the sensor system offers enhanced performance and linearity. The proposed sensor employs a semicircular-shaped rotor plate (RP) placed between two printed circuit board (PCBs) with four coils each. These coils are strategically designed to ensure a linear variation of inductance with respect to the RP position, resulting in improved linearity in the sensor output. After validating the sensor design through analytical methods and finite-element analysis (FEA), a suitable algorithm was developed for accurately estimating the rotor angle. A sensor prototype was manufactured to evaluate the performance of the sensor system. The prototype showed an excellent linearity with a worst case error of 0.31% and a resolution of 0.11°. The sensor shows negligible sensitivity to axial misalignment of the shaft and the presence of external magnetic objects, highlighting the practical usefulness of the system.
本文介绍了一种易于制造、全圆范围（0°-360°）、基于平面线圈的可变磁阻（VR）角度传感器，具有更高的线性度。拟议的传感器系统旨在缓解现有 VR 角度传感器的局限性，特别是其固有的传感器输出特性所导致的有限精度和非线性。通过精心设计线圈的几何形状以实现均匀的磁通量分布，并采用简单的半圆形转子，该传感器系统的性能和线性度都得到了提高。拟议的传感器采用了一个半圆形转子板（RP），置于两块印刷电路板（PCB）之间，每块印刷电路板有四个线圈。这些线圈经过精心设计，可确保电感随 RP 位置的线性变化，从而提高传感器输出的线性度。通过分析方法和有限元分析（FEA）对传感器设计进行验证后，开发出一种合适的算法，用于准确估算转子角度。为评估传感器系统的性能，制造了一个传感器原型。原型显示出极佳的线性度，最坏情况下误差为 0.31%，分辨率为 0.11°。传感器对轴的轴向偏差和外部磁性物体的灵敏度几乎可以忽略不计，突出了该系统的实用性。
{"title":"A Variable Reluctance-Based Planar Dual-Coil Angle Sensor With Enhanced Linearity","authors":"Anil Kumar Appukuttan Nair Syamala Amma;P.P. Narayanan;Jeshma Thalapil Vaheeda;Sreenath Vijayakumar","doi":"10.1109/TIM.2024.3451596","DOIUrl":"https://doi.org/10.1109/TIM.2024.3451596","url":null,"abstract":"An easy-to-fabricate, full circle range (0°–360°), planar coil-based variable reluctance (VR) angle transducer with enhanced linearity is presented in this article. The proposed sensor system aims to mitigate the limitations of the existing VR angle sensors, particularly their limited accuracy and nonlinearity, resulting from the inherent sensor output characteristics. By carefully designing the coil geometry to achieve uniform flux distribution and implementing a simple semicircular-shaped rotor, the sensor system offers enhanced performance and linearity. The proposed sensor employs a semicircular-shaped rotor plate (RP) placed between two printed circuit board (PCBs) with four coils each. These coils are strategically designed to ensure a linear variation of inductance with respect to the RP position, resulting in improved linearity in the sensor output. After validating the sensor design through analytical methods and finite-element analysis (FEA), a suitable algorithm was developed for accurately estimating the rotor angle. A sensor prototype was manufactured to evaluate the performance of the sensor system. The prototype showed an excellent linearity with a worst case error of 0.31% and a resolution of 0.11°. The sensor shows negligible sensitivity to axial misalignment of the shaft and the presence of external magnetic objects, highlighting the practical usefulness of the system.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-8"},"PeriodicalIF":5.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595032","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}
Pub Date : 2024-11-01DOI: 10.1109/TIM.2024.3481591
Xiangyang Yang;Haitao Hu;Donghua Xiao;Haidong Tao;Yitong Song;Zhengyou He
Accurate impedance measurements of the train and traction network are crucial for small-signal stability analysis of railway train-network system (RTNS). Although impedance measurement methods for four-quadrant converters (4QCs) in electric trains based on harmonic voltage disturbance injection have been proposed, few studies have investigated the impact of integrating a harmonic generator on RTNS stability. To address this issue, this article proposes a wideband harmonic disturbance generator (WHDG) and evaluates its impact on RTNS stability. The WHDG primarily comprises the back-to-back converter-based cascaded H-bridge (CHB) structure and a wideband coupling transformer. This generator can produce multifrequency perturbations with uniformly distributed spectrum energy. Subsequently, an accurate output impedance model is established based on the detailed topology and parameters of the WHDG. The model accounts for the impact of the dc impedance of the front-stage rectifier on the post-stage inverter. The close alignment between the modeling and simulation results demonstrates the accuracy of the deduced impedance model. Furthermore, an impedance-matching analysis of the RTNS with integrated WHDG is performed, indicating that the internal impedance of the WHDG weakens the stability of the tested RTNS. Finally, the effectiveness of the proposed WHDG is validated via a hardware-in-the-loop (HIL) experimental platform, and the impedance-matching analysis results are verified.
{"title":"Impedance-Matching Analysis of Wideband Harmonic Disturbance Generator for Railway Train-Network System","authors":"Xiangyang Yang;Haitao Hu;Donghua Xiao;Haidong Tao;Yitong Song;Zhengyou He","doi":"10.1109/TIM.2024.3481591","DOIUrl":"https://doi.org/10.1109/TIM.2024.3481591","url":null,"abstract":"Accurate impedance measurements of the train and traction network are crucial for small-signal stability analysis of railway train-network system (RTNS). Although impedance measurement methods for four-quadrant converters (4QCs) in electric trains based on harmonic voltage disturbance injection have been proposed, few studies have investigated the impact of integrating a harmonic generator on RTNS stability. To address this issue, this article proposes a wideband harmonic disturbance generator (WHDG) and evaluates its impact on RTNS stability. The WHDG primarily comprises the back-to-back converter-based cascaded H-bridge (CHB) structure and a wideband coupling transformer. This generator can produce multifrequency perturbations with uniformly distributed spectrum energy. Subsequently, an accurate output impedance model is established based on the detailed topology and parameters of the WHDG. The model accounts for the impact of the dc impedance of the front-stage rectifier on the post-stage inverter. The close alignment between the modeling and simulation results demonstrates the accuracy of the deduced impedance model. Furthermore, an impedance-matching analysis of the RTNS with integrated WHDG is performed, indicating that the internal impedance of the WHDG weakens the stability of the tested RTNS. Finally, the effectiveness of the proposed WHDG is validated via a hardware-in-the-loop (HIL) experimental platform, and the impedance-matching analysis results are verified.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-14"},"PeriodicalIF":5.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595836","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}
Blade vibration testing is crucial for understanding the dynamic behavior of rotating machinery. This article presents a theoretical analysis and experimental validation of electromagnetic excitation for blade vibration testing in static conditions. The study focuses on investigating the effect of electromagnets on static blades to establish a theoretical foundation. The Timoshenko beam theory is utilized to analyze the vibration parameters, including amplitude and frequency while considering associated uncertainties. The theoretical analysis is complemented by numerical modeling using the finite-element method and experimental measurements employing laser Doppler vibrometer (LDV). The results demonstrate the effectiveness of electromagnetic excitation in generating controlled vibrations in static blades. These findings provide valuable insights and serve as a basis for subsequent investigations into the behavior of blades during rotation. The mathematical model’s frequency estimation error was approximately 4% compared to numerical results, and the numerical amplitude results differed by 6.4% from the experimental measurements. These contributions enhance the understanding and design of blade vibration monitoring systems in rotating machinery and provide valuable information on the blade’s dynamic parameters for the calibration of blade tip timing (BTT) systems.
{"title":"Electromagnetic Excitation for Blade Vibration Analysis in Static Conditions: Theoretical Insights and Experimental Evaluation","authors":"Mohammed Lamine Mekhalfia;Pavel Procházka;Radislav Smid;Philip Bonello;Peter Russhard;Dušan Maturkanič;Mohamed Elsayed Mohamed;Eder Batista Tchawou Tchuisseu","doi":"10.1109/TIM.2024.3488153","DOIUrl":"https://doi.org/10.1109/TIM.2024.3488153","url":null,"abstract":"Blade vibration testing is crucial for understanding the dynamic behavior of rotating machinery. This article presents a theoretical analysis and experimental validation of electromagnetic excitation for blade vibration testing in static conditions. The study focuses on investigating the effect of electromagnets on static blades to establish a theoretical foundation. The Timoshenko beam theory is utilized to analyze the vibration parameters, including amplitude and frequency while considering associated uncertainties. The theoretical analysis is complemented by numerical modeling using the finite-element method and experimental measurements employing laser Doppler vibrometer (LDV). The results demonstrate the effectiveness of electromagnetic excitation in generating controlled vibrations in static blades. These findings provide valuable insights and serve as a basis for subsequent investigations into the behavior of blades during rotation. The mathematical model’s frequency estimation error was approximately 4% compared to numerical results, and the numerical amplitude results differed by 6.4% from the experimental measurements. These contributions enhance the understanding and design of blade vibration monitoring systems in rotating machinery and provide valuable information on the blade’s dynamic parameters for the calibration of blade tip timing (BTT) systems.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"73 ","pages":"1-8"},"PeriodicalIF":5.6,"publicationDate":"2024-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142672061","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}
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