IEEE Transactions on Instrumentation and Measurement最新文献

Image Tracking of Fire Extinguishing Jet Drop Point Based on Improved ENet and Robust Adaptive Cubature Kalman Filtering 基于改进型 ENet 和鲁棒性自适应立方卡尔曼滤波的灭火喷射落点图像跟踪技术

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2024-09-09 DOI: 10.1109/TIM.2024.3451590

Lu Pan;Wei Li;Jinsong Zhu;Zhengsheng Chen;Juxian Zhao;Zhongguan Liu

{"title":"Image Tracking of Fire Extinguishing Jet Drop Point Based on Improved ENet and Robust Adaptive Cubature Kalman Filtering","authors":"Lu Pan;Wei Li;Jinsong Zhu;Zhengsheng Chen;Juxian Zhao;Zhongguan Liu","doi":"10.1109/TIM.2024.3451590","DOIUrl":"https://doi.org/10.1109/TIM.2024.3451590","url":null,"abstract":"Accurate image tracking of fire extinguishing jets is crucial to achieving automatic firefighting. However, inevitable noise interference occurs during image processing, adversely affecting precise tracking. In order to address this issue, a method for tracking the jet drop point (JDP) of a fire extinguishing jet is proposed based on an improved efficient neural network (ENet) and robust adaptive cubature Kalman filter (CKF). A novel JDP image state transition model is established to construct the state space equations and depict the motion state of the JDP in images. A two-stage method for recognizing JDP is proposed, which includes an improved ENet and a directional progressive curve search method to enhance the accuracy of observation. A CKF based on the Huber function is proposed to improve the adaptability and robustness of the image tracking method, which takes into account the advantages of \u0000<inline-formula> <tex-math>$L1$ </tex-math></inline-formula>\u0000 and \u0000<inline-formula> <tex-math>$L2$ </tex-math></inline-formula>\u0000 norms. The updated formulas for the state and covariance matrices are derived. Furthermore, the tracking method is improved by the Sage-Husa method, which considers the unknown distribution of noise. Experiments on actual firefighting platforms demonstrate that the proposed method exhibits robustness and adaptability compared to traditional CKF.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142235967","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

The CTIgram: A Novel Optimal Demodulation Band Selection Method and Its Applications in Condition Monitoring of Rotating Machinery CTIgram：一种新颖的最佳解调波段选择方法及其在旋转机械状态监测中的应用

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2024-09-06 DOI: 10.1109/TIM.2024.3450104

Jifeng Sui;Chaoyong Ma;Zuhua Jiang;Kun Zhang;Yonggang Xu

{"title":"The CTIgram: A Novel Optimal Demodulation Band Selection Method and Its Applications in Condition Monitoring of Rotating Machinery","authors":"Jifeng Sui;Chaoyong Ma;Zuhua Jiang;Kun Zhang;Yonggang Xu","doi":"10.1109/TIM.2024.3450104","DOIUrl":"https://doi.org/10.1109/TIM.2024.3450104","url":null,"abstract":"Theil index is an indicator proposed in the field of economics, developed from the concept of information entropy, used to measure the degree of difference in a system, and it can consider both overall and local differences. This article explores the application of the Theil index in bearing fault diagnosis and proposes the correlation Theil index (CTI). In order to use this indicator in bearing fault diagnosis, a tower-shaped distribution diagram called CTIgram is established. CTIgram adopts an adaptive multilevel spectrum segmentation method, which can well obtain the center frequency and bandwidth of the fault signal and adaptively divide the frequency band. The frequency band selected by CTI often contains more periodic pulse information. This method has great advantages in extracting fault information from signals with noise. The proposed method is shown to be effective by the simulation signal, and it was proved by the bearing outer and inner ring fault signals that this method can be applied to bearing fault diagnosis. The comparison experiments with fast Kurtogram (FK) and Gini index (GI) demonstrated the superiority of the method.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142164987","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

Unsupervised Scale Network for Monocular Relative Depth and Visual Odometry 用于单眼相对深度和视觉方位测量的无监督标度网络

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2024-09-05 DOI: 10.1109/TIM.2024.3451584

Zhongyi Wang;Qijun Chen

{"title":"Unsupervised Scale Network for Monocular Relative Depth and Visual Odometry","authors":"Zhongyi Wang;Qijun Chen","doi":"10.1109/TIM.2024.3451584","DOIUrl":"https://doi.org/10.1109/TIM.2024.3451584","url":null,"abstract":"With the rapid development of deep learning and computer vision, learning-based monocular depth estimation and visual odometry have achieved increasingly remarkable results. However, there are few studies on the scale ambiguity of monocular depth estimation and visual odometry in an unsupervised network framework. Therefore, this article is to solve this thorny problem. We propose a joint unsupervised network framework that can provide necessary information to each other between different tasks to meet the needs of multiple tasks. To address the issue of scale ambiguity for learning-based monocular depth estimation, we propose a novel ScaleNet, an unsupervised scale network that provides scale information for the relative depths predicted by monocular depth networks, thereby recovering the absolute depths. Meanwhile, we propose a pseudo ground-truth scale generator and constrain the scale network by scale loss. The experimental results show that our monocular depth estimation results are competitive, and the scale network can provide reliable scale information for monocular depth networks. To address the challenge of scale ambiguity in learning-based monocular visual odometry, we propose a solution based on scale and optical flow to obtain the absolute scale of translational vectors using our depth alignment method. The experimental results show that our monocular visual odometry achieves state-of-the-art performance. The extensive experiments on the KITTI dataset for different tasks demonstrate the effectiveness and generalization of our proposed method.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142233576","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

Clustering Federated Learning for Wafer Defects Classification on Statistical Heterogeneous Data 在统计异构数据上进行晶圆缺陷分类的聚类联合学习

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2024-09-05 DOI: 10.1109/TIM.2024.3415785

Guang Yang;Zhijia Yang;Shuping Cui;Chunhe Song;Jizhou Wang;Haodong Wei

{"title":"Clustering Federated Learning for Wafer Defects Classification on Statistical Heterogeneous Data","authors":"Guang Yang;Zhijia Yang;Shuping Cui;Chunhe Song;Jizhou Wang;Haodong Wei","doi":"10.1109/TIM.2024.3415785","DOIUrl":"https://doi.org/10.1109/TIM.2024.3415785","url":null,"abstract":"Data-driven deep learning techniques for wafer defect image classification provide wafer manufacturers with a tool to rapidly identify surface defects. However, the defect data and computational capabilities of a single wafer manufacturer are often insufficient to support the training of deep learning models. In response, we introduce federated learning (FL), a paradigm that leverages the data and computational capabilities of various wafer manufacturers, all while ensuring that the original data from different manufacturers remain unexposed to each other. Due to variations in manufacturing processes and image acquisition equipment, identical wafer defects can exhibit different features in different manufacturing settings, leading to statistically heterogeneous datasets. This heterogeneity can reduce model convergence speed and accuracy. To counteract this issue, we propose a personalized FL approach with clustering. In the personalization phase, we train distinct network layers for each client’s local model, capitalizing on the feature extraction capability of the global model’s shallow network, while also achieving commendable performance on each client’s unique dataset. During the clustering phase, we provide a theoretical analysis, demonstrating that the divergence of weights between two models is bounded above, laying a theoretical foundation for the clustering operation. We then enhance a density-based clustering method, enabling the clustering of clients with similar data features without the need to specify the number of cluster centers, thus mitigating the problem of global model oscillation. We have conducted experiments under various data heterogeneity scenarios. The experiments show that our method can achieve a 2.8% accuracy improvement average versus the compared state-of-the-art federated methods with a faster convergence rate.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142169677","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

Unsupervised GAN With Fine-Tuning: A Novel Framework for Induction Motor Fault Diagnosis in Scarcely Labeled Sample Scenarios 带微调的无监督 GAN：用于稀缺标记样本场景中感应电机故障诊断的新型框架

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2024-09-03 DOI: 10.1109/TIM.2024.3446655

Xin Chen;Zaigang Chen;Shiqian Chen;Liming Wang;Wanming Zhai

{"title":"Unsupervised GAN With Fine-Tuning: A Novel Framework for Induction Motor Fault Diagnosis in Scarcely Labeled Sample Scenarios","authors":"Xin Chen;Zaigang Chen;Shiqian Chen;Liming Wang;Wanming Zhai","doi":"10.1109/TIM.2024.3446655","DOIUrl":"https://doi.org/10.1109/TIM.2024.3446655","url":null,"abstract":"Induction motor serves as the indispensable component of industrial equipment, where the failure of the key part can lead to significant losses, and its intelligent fault diagnosis (IFD) based on supervised deep learning methods requires extensive labeled data. However, acquiring sufficient labeled samples in industrial equipment usually remains a significant challenge. To tackle this problem, a novel semi-supervised IFD framework based on an improved unsupervised generative adversarial network (GAN) and fine-tuning is proposed. Specifically, an enhanced learning strategy is developed by introducing gradient normalization constraint and a hinge loss function into the modified GAN model to stabilize the adversarial training process and improve the discrimination, which can boost the model’s feature distribution learning ability among various unlabeled data categories. The discriminator trained by unlabeled samples is then employed to identify the fault after being fine-tuned with a limited number of labeled samples. Finally, the effectiveness of the proposed framework is verified by two induction motor cases, including mechanical and electrical failures. The results demonstrate that the proposed method has significant superiorities in training stability and identification accuracy under the condition of very few labeled samples.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142159959","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

Cascading Time-Frequency Transformer and Spatio-Temporal Graph Attention Network for Rotating Machinery Fault Diagnosis 用于旋转机械故障诊断的级联时频变压器和时空图注意网络

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2024-09-03 DOI: 10.1109/TIM.2024.3453312

Yiqi Liu;Zhewen Yu;Min Xie

{"title":"Cascading Time-Frequency Transformer and Spatio-Temporal Graph Attention Network for Rotating Machinery Fault Diagnosis","authors":"Yiqi Liu;Zhewen Yu;Min Xie","doi":"10.1109/TIM.2024.3453312","DOIUrl":"https://doi.org/10.1109/TIM.2024.3453312","url":null,"abstract":"Rotating machinery fault diagnosis is of great importance to guarantee safe and optimal operations of industrial processes. Heavy noise and dynamic behaviors usually make accurate mechanical fault diagnosis impossible while using the standard methodologies, particularly when they disregard specific domain information related to time, frequency, or space. To address these challenges, we propose a novel model, called spatio-temporal-frequency graph attention network (STFGAT), which can integrate time domain, frequency domain, and spatial information. The model leverages the Transformer to encode time and frequency information, then refined complex patterns in the time and frequency domain through self-attention mechanism and frequency domain attention, and finally captures the hidden patterns behind the data through the collaboration of time and frequency information. The encoded information is subsequently fed into the spatio-temporal graph attention network (STGAT) to allow the model to take full use of the spatial relationships between different components of the mechanical system and the temporal relationships across various time lags. This process improvement can learn complex patterns and relationships within the data, thereby facilitating predictions regarding the system’s state. The experimental results show that STFGAT outperforms other standard diagnostic models in the case studies and can achieve better diagnostic accuracy.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142235668","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

Underground Power Cable Fault Detection and Localization Based on the Spectrum of Propagation Functions 基于传播函数频谱的地下电力电缆故障检测与定位

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2024-09-03 DOI: 10.1109/TIM.2024.3453315

Chunqi Liu;Yimin Hou;Zhimin Duan;Rui Shang;Kunpeng Wang;Dongsheng Chen

{"title":"Underground Power Cable Fault Detection and Localization Based on the Spectrum of Propagation Functions","authors":"Chunqi Liu;Yimin Hou;Zhimin Duan;Rui Shang;Kunpeng Wang;Dongsheng Chen","doi":"10.1109/TIM.2024.3453315","DOIUrl":"https://doi.org/10.1109/TIM.2024.3453315","url":null,"abstract":"Multiple potential defects in cables cannot be detected and localized due to the low detection sensitivity of the time-domain reflectometry (TDR) method, as well as the cross-term interference problem of the time- and frequency-domain reflectometry method. A new method for fault detection in underground power cables is proposed based on the spectrum of propagation function. First, the propagation characteristics of various frequency signals in cables are analyzed to identify the causes of local irregularities in the spectrum of the propagation function (SPF) resulting from defects. Furthermore, the locations of defects are successfully identified using the integral transform algorithm. As broadband impedance spectroscopy (BIS) cannot accurately measure the input impedance of a 163 m cable for defect detection, the spectrum of propagation function is more suitable for long-distance cables. Finally, cables of 50, 66, and 163 m lengths are examined for mechanical damage and thermal aging defects. The fault localization error is within 0.21%, providing a technical reference for fault detection of underground cables.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142235812","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

Sparse Signal Phase Retrieval for Phaseless Short-Time Fourier Transform Measurement Based on Local Search 基于局部搜索的无相位短时傅里叶变换测量的稀疏信号相位检索

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2024-09-02 DOI: 10.1109/TIM.2024.3451592

Xiaodong Li;Pinjun Zheng;Ning Fu;Liyan Qiao;Tareq Y. Al-Naffouri

{"title":"Sparse Signal Phase Retrieval for Phaseless Short-Time Fourier Transform Measurement Based on Local Search","authors":"Xiaodong Li;Pinjun Zheng;Ning Fu;Liyan Qiao;Tareq Y. Al-Naffouri","doi":"10.1109/TIM.2024.3451592","DOIUrl":"https://doi.org/10.1109/TIM.2024.3451592","url":null,"abstract":"The sparse signal phase retrieval (PR) for phaseless short-time Fourier transform (STFT) measurement is a crucial problem manifesting across various applications. The existing solutions involve amplitude and support estimation. Amplitude estimation, a nonlinear least squares problem, faces issues due to the not full rank of the derivative matrix associated with the objective function. Existing support estimation relies on random initialization, reducing accuracy and noise robustness. To address these, a novel phaseless measurement structure and the corresponding solution framework are proposed. Initially, a measurements preprocessing algorithm is employed, utilizing the properties of the measurement matrix to efficiently reduce the dimensions of the solution. Subsequently, a support estimation algorithm based on local search is developed, where the support preestimation takes advantage of the sparse support characteristics. In addition, an amplitude estimation algorithm, utilizing the trust region method, is proposed. The proposed algorithm’s effectiveness and its superiority in accuracy and noise robustness over existing methods are demonstrated through numerical simulations and hardware experiments.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142233577","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

Geometric Consistency-Guaranteed Spatio-Temporal Transformer for Unsupervised Multiview 3-D Pose Estimation 用于无监督多视角三维姿态估计的几何一致性保证时空变换器

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2024-09-02 DOI: 10.1109/TIM.2024.3440376

Kaiwen Dong;Kévin Riou;Jingwen Zhu;Andréas Pastor;Kévin Subrin;Yu Zhou;Xiao Yun;Yanjing Sun;Patrick Le Callet

{"title":"Geometric Consistency-Guaranteed Spatio-Temporal Transformer for Unsupervised Multiview 3-D Pose Estimation","authors":"Kaiwen Dong;Kévin Riou;Jingwen Zhu;Andréas Pastor;Kévin Subrin;Yu Zhou;Xiao Yun;Yanjing Sun;Patrick Le Callet","doi":"10.1109/TIM.2024.3440376","DOIUrl":"https://doi.org/10.1109/TIM.2024.3440376","url":null,"abstract":"Unsupervised 3-D pose estimation has gained prominence due to the challenges in acquiring labeled 3-D data for training. Despite promising progress, unsupervised approaches still lag behind supervised methods in performance. Two factors impede the progress of unsupervised approaches: incomplete geometric constraint and inadequate interaction among spatial, temporal, and multiview features. This article introduces an unsupervised pipeline that uses calibrated camera parameters as geometric constraints across views and coordinate spaces to optimize the model by minimizing inconsistencies between the 2-D input pose and the reprojection of the predicted 3-D pose. This pipeline utilizes the novel hierarchical cross transformer (HCT) to encode higher levels of information by enabling interactions among hierarchical features containing different levels of temporal, spatial, and cross-view information. By minimizing the reliance on human-specific parts, the HCT shows potential for adapting to various pose estimation tasks. To validate the adaptability, we build a connection between human pose estimation and scene pose estimation, introducing a dynamic-keypoints-3-D (DKs-3D) dataset tailored for 3-D scene pose estimation in robotic manipulation. Experiments on two 3-D human pose estimation datasets demonstrate our method’s new state-of-the-art performance among weakly and unsupervised approaches. The adaptability of our method is confirmed through experiments on DK-3D, setting the initial benchmark for unsupervised 2-D-to-3-D scene pose lifting.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142233581","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

SSGC-GAT: Synergistic Similarity Graph Construction Strategy Combined With GAT Network for Wind Turbine Anomaly Identification Using SCADA Data SSGC-GAT：结合 GAT 网络的协同相似图构建策略，利用 SCADA 数据进行风力涡轮机异常识别

IF 5.6 2区工程技术

IEEE Transactions on Instrumentation and Measurement Pub Date : 2024-09-02 DOI: 10.1109/TIM.2024.3453323

Xiaomin Wang;Xiao Zhuang;Jian Ge;Jiawei Xiang;Di Zhou

{"title":"SSGC-GAT: Synergistic Similarity Graph Construction Strategy Combined With GAT Network for Wind Turbine Anomaly Identification Using SCADA Data","authors":"Xiaomin Wang;Xiao Zhuang;Jian Ge;Jiawei Xiang;Di Zhou","doi":"10.1109/TIM.2024.3453323","DOIUrl":"https://doi.org/10.1109/TIM.2024.3453323","url":null,"abstract":"The supervisory control and data acquisition (SCADA) system is the standard installation on large wind turbine (WT) to monitor all major WT subcomponents. By analyzing SCADA data, the anomaly of the WT can be timely identified. However, the complex coupling relationship between different sensors poses a great challenge to the high accuracy of WT anomaly identification. In this article, a novel synergistic similarity graph construction (SSGC)-graph attention network (GAT) method that integrates the SSGC strategy into GAT is proposed to realize high-accuracy anomaly identification of WT. The GAT has a strong graph data modeling capability to accurately capture important relationships between nodes. Furthermore, the proposed SSGC strategy constructs similar graph data by fusing the adjacency matrices computed by four different methods. The SSGC strategy can adaptively learn the complex relationships among multiple parameters to improve the accuracy of anomaly identification. A large number of experiments are conducted to verify the effectiveness and superiority of the proposed SSGC-GAT. The experimental results show that, compared with other several benchmark methods, the proposed SSGC-GAT has the best identification performance. In addition, the ablation experiment results demonstrate that the proposed SSGC strategy can effectively improve the accuracy of WT anomaly identification.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":null,"pages":null},"PeriodicalIF":5.6,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142235810","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