{"title":"一种绝缘子并联间隙解耦分层故障检测方法:结合轻量定位和基于注意力的诊断","authors":"Shuai Hao;Tianqi Li;Xu Ma;Shiao Fan;Tianrui Qi","doi":"10.1109/TIM.2025.3604115","DOIUrl":null,"url":null,"abstract":"Insulator parallel gaps, as critical lightning protection components in high-voltage transmission lines, are prone to faults, such as short circuits and excessive spacing, which compromise line safety and power supply reliability. However, detecting insulator parallel gaps from unmanned aerial vehicle (UAV) captured images is challenged by complex backgrounds, structural distortion, and occlusion. Thus, a hierarchical detection method incorporating lightweight localization and attention mechanism-based diagnosis (KLSD-HDet) is proposed, which uses a localization network to capture the faults and then conducts fault diagnosis. First, to accurately capture target objects, a lightweight fault localization network (KDeFus-LNet) is designed, with nonlinear feature extraction (NFE) and target localization capabilities in complex backgrounds enhanced. Second, leveraging the spatial geometric information from 3-D fault models, a multiview fault image generation method is developed to compensate for the missing feature representations of partial viewpoints in real-world datasets. Then, a cross-space learning and multiscale residual-based fault diagnosis network (S-CR2-DNet) is proposed to improve multiview fault representation understanding and diagnostic accuracy. Finally, knowledge distillation is employed to lightweight S-CR2-DNet, enhancing its practical applicability. Extensive experiments validate that KLSD-HDet outperforms the state-of-the-art (SOTA) methods, achieving 94.49% detection precision, improved by 6.65% compared to the SOTA algorithms.","PeriodicalId":13341,"journal":{"name":"IEEE Transactions on Instrumentation and Measurement","volume":"74 ","pages":"1-15"},"PeriodicalIF":5.9000,"publicationDate":"2025-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A Decoupled Hierarchical Fault Detection Method for Insulator Parallel Gaps: Integrating Lightweight Localization and Attention-Based Diagnosis\",\"authors\":\"Shuai Hao;Tianqi Li;Xu Ma;Shiao Fan;Tianrui Qi\",\"doi\":\"10.1109/TIM.2025.3604115\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Insulator parallel gaps, as critical lightning protection components in high-voltage transmission lines, are prone to faults, such as short circuits and excessive spacing, which compromise line safety and power supply reliability. However, detecting insulator parallel gaps from unmanned aerial vehicle (UAV) captured images is challenged by complex backgrounds, structural distortion, and occlusion. Thus, a hierarchical detection method incorporating lightweight localization and attention mechanism-based diagnosis (KLSD-HDet) is proposed, which uses a localization network to capture the faults and then conducts fault diagnosis. First, to accurately capture target objects, a lightweight fault localization network (KDeFus-LNet) is designed, with nonlinear feature extraction (NFE) and target localization capabilities in complex backgrounds enhanced. Second, leveraging the spatial geometric information from 3-D fault models, a multiview fault image generation method is developed to compensate for the missing feature representations of partial viewpoints in real-world datasets. Then, a cross-space learning and multiscale residual-based fault diagnosis network (S-CR2-DNet) is proposed to improve multiview fault representation understanding and diagnostic accuracy. Finally, knowledge distillation is employed to lightweight S-CR2-DNet, enhancing its practical applicability. Extensive experiments validate that KLSD-HDet outperforms the state-of-the-art (SOTA) methods, achieving 94.49% detection precision, improved by 6.65% compared to the SOTA algorithms.\",\"PeriodicalId\":13341,\"journal\":{\"name\":\"IEEE Transactions on Instrumentation and Measurement\",\"volume\":\"74 \",\"pages\":\"1-15\"},\"PeriodicalIF\":5.9000,\"publicationDate\":\"2025-09-02\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Transactions on Instrumentation and Measurement\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/11146696/\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Transactions on Instrumentation and Measurement","FirstCategoryId":"5","ListUrlMain":"https://ieeexplore.ieee.org/document/11146696/","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
A Decoupled Hierarchical Fault Detection Method for Insulator Parallel Gaps: Integrating Lightweight Localization and Attention-Based Diagnosis
Insulator parallel gaps, as critical lightning protection components in high-voltage transmission lines, are prone to faults, such as short circuits and excessive spacing, which compromise line safety and power supply reliability. However, detecting insulator parallel gaps from unmanned aerial vehicle (UAV) captured images is challenged by complex backgrounds, structural distortion, and occlusion. Thus, a hierarchical detection method incorporating lightweight localization and attention mechanism-based diagnosis (KLSD-HDet) is proposed, which uses a localization network to capture the faults and then conducts fault diagnosis. First, to accurately capture target objects, a lightweight fault localization network (KDeFus-LNet) is designed, with nonlinear feature extraction (NFE) and target localization capabilities in complex backgrounds enhanced. Second, leveraging the spatial geometric information from 3-D fault models, a multiview fault image generation method is developed to compensate for the missing feature representations of partial viewpoints in real-world datasets. Then, a cross-space learning and multiscale residual-based fault diagnosis network (S-CR2-DNet) is proposed to improve multiview fault representation understanding and diagnostic accuracy. Finally, knowledge distillation is employed to lightweight S-CR2-DNet, enhancing its practical applicability. Extensive experiments validate that KLSD-HDet outperforms the state-of-the-art (SOTA) methods, achieving 94.49% detection precision, improved by 6.65% compared to the SOTA algorithms.
期刊介绍:
Papers are sought that address innovative solutions to the development and use of electrical and electronic instruments and equipment to measure, monitor and/or record physical phenomena for the purpose of advancing measurement science, methods, functionality and applications. The scope of these papers may encompass: (1) theory, methodology, and practice of measurement; (2) design, development and evaluation of instrumentation and measurement systems and components used in generating, acquiring, conditioning and processing signals; (3) analysis, representation, display, and preservation of the information obtained from a set of measurements; and (4) scientific and technical support to establishment and maintenance of technical standards in the field of Instrumentation and Measurement.