IEEE Transactions on Instrumentation and Measurement最新文献_第2页

Spatial–Temporal–Geometric Graph Convolutional Network for 3-D Human Pose Estimation From Multiview Video

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-03-24 DOI: 10.1109/TIM.2025.3551025

Kaiwen Dong;Yu Zhou;Kévin Riou;Xiao Yun;Yanjing Sun;Kévin Subrin;Patrick Le Callet

{"title":"Spatial–Temporal–Geometric Graph Convolutional Network for 3-D Human Pose Estimation From Multiview Video","authors":"Kaiwen Dong;Yu Zhou;Kévin Riou;Xiao Yun;Yanjing Sun;Kévin Subrin;Patrick Le Callet","doi":"10.1109/TIM.2025.3551025","DOIUrl":"https://doi.org/10.1109/TIM.2025.3551025","url":null,"abstract":"The multiview 3-D human pose estimation (HPE) effectively addresses challenges, such as depth ambiguity and occlusion faced by monocular methods through the complementing of geometric information from multiple views. However, existing multiview methods often necessitate well-calibrated camera parameters or rely on complex parametric models. These requirements can result in inaccuracies when camera placement is perturbed and can negatively impact the deployability. This article proposes a lightweight approach that synergistically models geometric information with spatial-temporal information without relying on camera parameters, named spatial-temporal–geometric graph convolutional network (STG-GCN). We leverage the inherent connections in multiview sequences of 2-D poses, representing them as a spatial-temporal–geometric graph (STG-Graph), which allows for the simultaneous encoding of spatial-temporal–geometric relations across various joints, consecutive frames, and multiple views. Using a unified graph to model all features, this approach reduces the parameter explosion in existing methods, caused by separate modules extracting spatial, temporal, and view axis features. Building upon the STG-Graph, an adaptive confidence-aware graph convolution (ACA-GraphConv) is proposed to mitigate the impact of unreliable 2-D poses predicted by 2-D pose estimators. This is achieved by leveraging corresponding confidence scores to adjust the graph convolution accordingly. Experimental results on two public datasets demonstrate that our STG-GCN achieves performance comparable to state-of-the-art approaches while significantly reducing parameter volume. Ablation studies also illustrate the effectiveness of our ACA-GraphConv in both monocular and multiview scenarios.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.6,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726388","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

Contactless Wideband Current Measurement Based on Tunneling Magnetoresistance Sensor Array for Rectangular Busbar Systems

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-03-21 DOI: 10.1109/TIM.2025.3551027

Qi Zhu;Guangchao Geng;Quanyuan Jiang

{"title":"Contactless Wideband Current Measurement Based on Tunneling Magnetoresistance Sensor Array for Rectangular Busbar Systems","authors":"Qi Zhu;Guangchao Geng;Quanyuan Jiang","doi":"10.1109/TIM.2025.3551027","DOIUrl":"https://doi.org/10.1109/TIM.2025.3551027","url":null,"abstract":"Current measurements based on magnetic field sensor arrays have attracted a lot of attention due to their convenience in installation and maintenance. However, conventional current transducers of rectangular busbars are primarily designed for the dc and 50-Hz currents. Numerous industrial applications in power systems require novel technologies of ac current transducers with broader frequency ranges. Some research has achieved accurate measurement for high-frequency ac currents but often relies heavily on the known relative positions of the busbars and sensors. Thus, this article proposes a wideband current measurement method without prior knowledge of precise busbar and sensor positions, which consists of two steps. The first step is using the sensed magnetic field at the fundamental frequency to localize busbar positions, which assist in revealing the regularity of magnetic field variation with frequency. The second step is to reconstruct the currents at harmonic frequencies using the sensed magnetic field at different frequencies and busbar positions. The proposed method is demonstrated for the experiments involving different arrangements of busbar systems, and the performance of the designed wideband current measuring device is verified through experiments.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.6,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143726411","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

Fault-Tolerant Placement of Phasor Measurement Units and Communication Infrastructure in SDN-Enabled Wide-Area Monitoring Systems

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-03-21 DOI: 10.1109/TIM.2025.3551007

Huibin Jia;Weiran Hou;Siqi Wan;Xuguang Wang;Hongyin Xiang

{"title":"Fault-Tolerant Placement of Phasor Measurement Units and Communication Infrastructure in SDN-Enabled Wide-Area Monitoring Systems","authors":"Huibin Jia;Weiran Hou;Siqi Wan;Xuguang Wang;Hongyin Xiang","doi":"10.1109/TIM.2025.3551007","DOIUrl":"https://doi.org/10.1109/TIM.2025.3551007","url":null,"abstract":"Software-defined networking (SDN) separates the control plane from the data plane, features flexible configuration and dynamic programmability, and can enhance the survivability of networks during faults. SDN has been widely applied in wide-area monitoring systems (WAMSs) to enhance their fault defense capabilities. This study investigated the fault-tolerant placement problem of phasor measurement units (PMUs) and communication infrastructure of SDN-enabled WAMSs. First, a hidden Markov model was established to describe the recovery process of observable states of power nodes when the failures of power lines and communication networks occur. Based on the hidden Markov model, the availability of synchrophasors of the power node was calculated. Second, a mathematical model for the simultaneously optimal placement of the PMUs and SDN infrastructure was formulated to minimize the construction cost of the WAMS. The mathematical model was calculated using a particle swarm optimization (PSO) algorithm. Finally, simulation experiments were performed on an IEEE 30-bus system and an IEEE 118-bus system to verify the effectiveness of the proposed method. Experimental results indicate that the proposed method can effectively improve the accuracy of the state estimation, which enhances the ability of WAMSs to mitigate failure risks.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-11"},"PeriodicalIF":5.6,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706758","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 State-Matching-Based Method for Identifying Intrusive Object Data and Evaluating Collision Features Using Robotic E-Skin Proximity Perception

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-03-21 DOI: 10.1109/TIM.2025.3551000

Guangming Xue;Guodong Chen;Lining Sun;Huicong Liu

{"title":"A State-Matching-Based Method for Identifying Intrusive Object Data and Evaluating Collision Features Using Robotic E-Skin Proximity Perception","authors":"Guangming Xue;Guodong Chen;Lining Sun;Huicong Liu","doi":"10.1109/TIM.2025.3551000","DOIUrl":"https://doi.org/10.1109/TIM.2025.3551000","url":null,"abstract":"Proximity perception is a crucial foundation for robotic collision safety control, and e-skin proximity offers unique advantages in this field. However, traditional e-skin proximity data struggle to effectively distinguish between intrusive object data and the data from the robot itself and the surrounding environment, making it accurately evaluate collision features of intrusive objects. This article proposes a state-matching-based method for identifying intrusive object data and evaluating collision features using e-skin proximity. By establishing a nonintrusive feature model, the process extracts the nonintrusive feature data corresponding to the current robot state through state matching and compares it with the current e-skin proximity data. This allows for the effective identification of intrusive object data and the accurate and rapid evaluation of collision features, such as approach distance (AD) and approach orientation (AO). In the static experiments, the proposed method significantly improves the accuracy of evaluating AD and AO. In the dynamic experiments, the method proposed in this article demonstrated a high degree of alignment between the evaluated values and the actual values for AD and AO. Furthermore, this article analyzes the impact of the sampling state differentiation (SD) threshold during the construction of the nonintrusive feature (NIF) model on the subsequent evaluation of the robot’s dynamic AD. It demonstrates that a lower threshold for sampling SD threshold will effectively enhance the stability of the robot’s dynamic feature evaluation. Through experiments on safe collision control of robots along predetermined trajectory, it is proven that the method proposed in this article can achieve safe collision speed control for robots in human-robot interaction (HRI) scenarios, where the robot operates at a speed of 0.5 m/s.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143706701","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

Estimating Remaining Useful Life of Aircraft Engine System via a Novel Graph Tensor Fusion Network Based on Knowledge of Physical Structure and Thermodynamics

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-03-21 DOI: 10.1109/TIM.2025.3550613

Ze-Zhou Liu;Tao Sun;Xi-Ming Sun;Wen-Yue Cui

{"title":"Estimating Remaining Useful Life of Aircraft Engine System via a Novel Graph Tensor Fusion Network Based on Knowledge of Physical Structure and Thermodynamics","authors":"Ze-Zhou Liu;Tao Sun;Xi-Ming Sun;Wen-Yue Cui","doi":"10.1109/TIM.2025.3550613","DOIUrl":"https://doi.org/10.1109/TIM.2025.3550613","url":null,"abstract":"Accurate estimation of the remaining useful life (RUL) of aircraft engines is critical for aircraft health management and maintenance planning. To address such an issue, this article proposes a spatiotemporal graph attention tensor network (STGATN) based on knowledge of physical structure and thermodynamics. First, by utilizing engine sensor time-series data, we generate and construct an airflow state graph with thermodynamic knowledge and a structure state graph with structural layout knowledge. Then, by introducing a graph attention mechanism to extract spatial features of the two types of state graphs separately, and by proposing a tensor fusion module to embed and integrate the two groups of first-order feature vectors into a high-order tensor data. Furthermore, by designing the convLSTM layer to acquire temporal information of high-order tensor for accurate RUL prediction. Finally, experiments are conducted on the commercial modular aero-propulsion system simulation (CMPASS) dataset and the real engine test dataset. The comparative results show that our approach outperforms existing state-of-the-art methods.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-14"},"PeriodicalIF":5.6,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735291","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

Tensor Low-Rank Approximation via Plug-and-Play Priors for Anomaly Detection in Remote Sensing Images

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-03-21 DOI: 10.1109/TIM.2025.3553235

Jingjing Liu;Manlong Feng;Xianchao Xiu;Xiaoyang Zeng;Jianhua Zhang

{"title":"Tensor Low-Rank Approximation via Plug-and-Play Priors for Anomaly Detection in Remote Sensing Images","authors":"Jingjing Liu;Manlong Feng;Xianchao Xiu;Xiaoyang Zeng;Jianhua Zhang","doi":"10.1109/TIM.2025.3553235","DOIUrl":"https://doi.org/10.1109/TIM.2025.3553235","url":null,"abstract":"Optical remote sensing images (RSIs) have received widespread attention in fields such as agricultural monitoring, mineral exploration, and military defense. However, the detection performance will be seriously degraded when interfered with by noise. To overcome this issue, we first present a novel method called tensor low-rank approximation (TLRA), which leverages the weighted tensor nuclear norm (WTNN) to exploit the spectral overall structure, introduces a new tensor sparse <inline-formula> <tex-math>$ell _{F,0}$ </tex-math></inline-formula> term to characterize the local anomalies, and embeds an auxiliary <inline-formula> <tex-math>$ell _{F}$ </tex-math></inline-formula> term to reduce the impact of Gaussian noise. Compared to existing tensor low-rank methods, the proposed TLRA has shown improvements in feature recognition performance and robustness. Moreover, by integrating pretrained neural networks instead of the WTNN, we further construct a plug-and-play (PnP) deep prior variant, dubbed PnP-TLRA, which can automatically learn nonlocal self-similarity. In addition, we have devised a consolidated optimization strategy utilizing the alternating direction method of multipliers (ADMM). The numerical experiments verify the advantages of the proposed methods over benchmark detectors and also show that PnP-TLRA has a better performance compared to TLRA with respect to effectiveness, efficiency, separability, and convergence. The code of the proposed methods will be published at <uri>https://github.com/EMXlight</uri>.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-14"},"PeriodicalIF":5.6,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748949","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

PRAISE-Net: Deep Projection-Domain Data-Consistent Learning Network for CBCT Metal Artifact Reduction

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-03-21 DOI: 10.1109/TIM.2025.3551446

Zhan Wu;Xinyun Zhong;Tianling Lyu;Yan Xi;Xu Ji;Yi Zhang;Shipeng Xie;Hengyong Yu;Yang Chen

{"title":"PRAISE-Net: Deep Projection-Domain Data-Consistent Learning Network for CBCT Metal Artifact Reduction","authors":"Zhan Wu;Xinyun Zhong;Tianling Lyu;Yan Xi;Xu Ji;Yi Zhang;Shipeng Xie;Hengyong Yu;Yang Chen","doi":"10.1109/TIM.2025.3551446","DOIUrl":"https://doi.org/10.1109/TIM.2025.3551446","url":null,"abstract":"High-attenuation metal implants can cause metal artifacts in cone-beam computed tomography (CBCT) scanning due to their strong and energy-dependent photon-absorption ability. These artifacts severely degrade image quality in intraoperative radiotherapy and postoperative diagnosis for clinical physicians. The conventional projection-domain metal artifact reduction (MAR) methods for fan-beam geometry are not efficiently applicable to CBCT MAR, because metallic implants in cone-shaped X-ray beam scanning lead to serious data missing at all the projection views. To tackle the aforementioned challenge, we present a novel projection-domain data-consistent learning network, i.e., PRAISE-Net, to suppress CBCT metal artifacts. First, a Low2High strategy which inpaints metal traces at low resolution and restores high-resolution results with a super-resolution reconstruction (SRR) module is proposed to reduce computational costs. Second, a PIG-DDPM module with the prior knowledge is designed for fine-grained projection-domain metal area inpainting. Third, a CBCT domain adaptation (CBCT-DA) is incorporated in the PIG-DDPM to step across the gap between simulated data and clinical CBCT data. The proposed PRAISE-Net is trained and evaluated on a publicly available dataset and a private clinical CBCT dataset, and our results confirm that the proposed method outperforms the state-of-the-art competing methods. This efficient, accurate, and reliable CBCT MAR technique has a great potential for clinical applications.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-13"},"PeriodicalIF":5.6,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143761559","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

Seizure Detection Framework via Multisubject Dynamic Adaptation and Structural Clustering

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-03-21 DOI: 10.1109/TIM.2025.3551437

Xiaonan Cui;Dinghan Hu;Xiaoping Lai;Tiejia Jiang;Feng Gao;Jiuwen Cao

{"title":"Seizure Detection Framework via Multisubject Dynamic Adaptation and Structural Clustering","authors":"Xiaonan Cui;Dinghan Hu;Xiaoping Lai;Tiejia Jiang;Feng Gao;Jiuwen Cao","doi":"10.1109/TIM.2025.3551437","DOIUrl":"https://doi.org/10.1109/TIM.2025.3551437","url":null,"abstract":"Intersubject variation seriously affects the generalization ability of seizure detection models. Most current models need to be calibrated and trained with annotated data before application, making them strongly dependent on subject-specific features and difficult to directly generalize on new subjects. To overcome this limitation, we propose a multisubject dynamic adaptation and structural clustering (SCMDA) framework to perform offline seizure detection tasks. First, the backbone network is designed as a combination of the temporal encoder and multiple dynamic attention transfer (DAT) modules, where DAT is a parallel structure of squeeze-and-excitation (SE) residual and dynamic residual transfer (DRT). The designed DAT module can enhance the discriminability of the latent space and blur the distribution boundaries between source subjects to reduce the negative impact of domain information on distribution alignment. Then, the model is optimized by jointly discriminative feature alignment of the latent space and structurally regularized clustering of the target domain. The cluster centroids are generated by learning the self-attention feature interaction of the target data in a feedforward manner. Finally, to evaluate the effectiveness of SCMDA, we conduct extensive tests on the public available TUH dataset and the Children’s Hospital, Zhejiang University School of Medicine (CHZU) dataset. The proposed method achieves 93.42% and 91.23% cross-subject classification accuracy on the TUH and CHZU datasets, outperforming the current state-of-the-art offline algorithms.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740363","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

CO Emission Prediction for MSWI Process Based on Dual-Space Nested Dual-Window Drift Detection

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-03-21 DOI: 10.1109/TIM.2025.3551471

Runyu Zhang;Jian Tang;Heng Xia;Wen Yu;Junfei Qiao

{"title":"CO Emission Prediction for MSWI Process Based on Dual-Space Nested Dual-Window Drift Detection","authors":"Runyu Zhang;Jian Tang;Heng Xia;Wen Yu;Junfei Qiao","doi":"10.1109/TIM.2025.3551471","DOIUrl":"https://doi.org/10.1109/TIM.2025.3551471","url":null,"abstract":"The concentration of carbon monoxide (CO) emissions is intricately linked to the operational status and combustion efficiency of municipal solid waste incineration (MSWI) processes, which are characterized by complex, dynamic, and time-varying behaviors. In order to tackle the challenge of predicting CO emissions, this article introduces a novel method based on nested dual-window drift detection (NDWDD). Initially, a typical sample pool (TSP) is generated using the <inline-formula> <tex-math>$k$ </tex-math></inline-formula>-means algorithm. An offline prediction model combining long short-term memory (LSTM) with a feature space drift detection model based on robust principal component analysis (RPCA) is then developed. The control limit for error space prediction accuracy is set using the fast Hoeffding drift detection method (FHDDM). The NDWDD employs a unique combination of external feature space drift detection and nonparametric drift detection within the internal error space, using a nested mechanism to enhance detection efficiency and reduce the influence of inherent noise factors in industrial processes. Finally, the dual-space drift sample collection facilitates updates to the TSP, historical prediction models, RPCA model, and FHDDM control limits. Experimental results from a Beijing MSWI power plant demonstrate that the proposed method can predict CO emissions both robustly and effectively.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-15"},"PeriodicalIF":5.6,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143748950","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

Quantitative Diagnosis of Early Internal Short Circuit for Lithium-Ion Batteries Based on Multirate Discharge 基于多态放电的锂离子电池早期内部短路定量诊断方法

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2025-03-21 DOI: 10.1109/TIM.2025.3551580

Xiaoyu Chen;Yifeng Feng;Jiani Shen;Yijun He

{"title":"Quantitative Diagnosis of Early Internal Short Circuit for Lithium-Ion Batteries Based on Multirate Discharge","authors":"Xiaoyu Chen;Yifeng Feng;Jiani Shen;Yijun He","doi":"10.1109/TIM.2025.3551580","DOIUrl":"https://doi.org/10.1109/TIM.2025.3551580","url":null,"abstract":"Accurate quantitative diagnosis of early internal short circuit (ISC) can provide an important indication for thermal runaway (TR) in lithium-ion batteries (LIBs). However, both electrical and thermal characteristics are not obvious in the early stage of ISC, traditional quantitative diagnosis methods usually employ reference battery or state estimation models to amplify the influence of ISC on battery characteristics, which consequently hinders practical application. In this article, a novel quantitative ISC diagnosis method, in which a multirate discharge test strategy is designed to amplify the effect of ISC self-discharge on measured capacity, is proposed to avoid dependency on the reference battery and state estimation model. Peukert’s equation is modified to extract the current-capacity relation under ISC and multirate discharge curves are used to identify the ISC resistance. Furthermore, the discharge current rate and voltage window are selected based on multiobjective optimization. The effectiveness of the proposed method is verified by batteries with different aging states and temperatures under multiple ISC conditions. The experimental results show the maximum estimation error is less than 5.5% with the ISC resistance values ranging from 10 to <inline-formula> <tex-math>$200~Omega $ </tex-math></inline-formula>. It implies that the proposed method could provide a promising idea for safety early warning of LIBs.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-12"},"PeriodicalIF":5.6,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143740414","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