基于混合监督对比学习和支持向量机框架的鲁棒开集局部放电诊断

IF 5.9 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
H.P.D.Shiran Madhuranga, Wong Jee Keen Raymond, Hazlee Azil Illias, Nurulafiqah Nadzirah Binti Mansor
{"title":"基于混合监督对比学习和支持向量机框架的鲁棒开集局部放电诊断","authors":"H.P.D.Shiran Madhuranga,&nbsp;Wong Jee Keen Raymond,&nbsp;Hazlee Azil Illias,&nbsp;Nurulafiqah Nadzirah Binti Mansor","doi":"10.1016/j.asej.2025.103762","DOIUrl":null,"url":null,"abstract":"<div><div>Automated partial discharge (PD) diagnosis using machine learning models is useful for high-voltage equipment (HVE) insulation condition monitoring. However, without a mechanism to identify unknown PD classes (defined as new classes not present in the training data), models will misclassify unknown classes as one of the known classes. To address this, a novel hybrid open-set recognition (OSR) framework based on Supervised Contrastive Learning (SupCon) is proposed to address a previously unexplored direction in the PD diagnosis domain. The framework integrates discriminative representation learning with both unified and per-class rejection strategies using one-class classification, enabling effective separation of known and unknown PD classes. The main contribution is the synergistic integration of SupCon for constructing structured latent spaces, SVM for precise closed-set classification, and dual OCSVMs for adaptive unknown rejection, together forming a unified pipeline that achieves both fine-grained discrimination and robust unknown detection. To evaluate the effectiveness of the proposed framework, comprehensive experiments are conducted across 30 OSR tasks, covering 12 PD classes from three types of high-voltage equipment under varying openness levels. The proposed framework is benchmarked against five state-of-the-art approaches, including ArcFace, GAN-Flow, a convolutional neural network (CNN), Autoencoder, and Vision Transformer. Experimental results demonstrate that the proposed framework achieved the best performance, with a mean normalized accuracy of 97.66 % and a Youden’s index of 0.953, confirming its robustness, generalization capability, and potential to advance open-set PD diagnostic methodologies.</div></div>","PeriodicalId":48648,"journal":{"name":"Ain Shams Engineering Journal","volume":"16 12","pages":"Article 103762"},"PeriodicalIF":5.9000,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Robust open-set partial discharge diagnosis based on hybrid supervised contrastive learning and SVM framework\",\"authors\":\"H.P.D.Shiran Madhuranga,&nbsp;Wong Jee Keen Raymond,&nbsp;Hazlee Azil Illias,&nbsp;Nurulafiqah Nadzirah Binti Mansor\",\"doi\":\"10.1016/j.asej.2025.103762\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Automated partial discharge (PD) diagnosis using machine learning models is useful for high-voltage equipment (HVE) insulation condition monitoring. However, without a mechanism to identify unknown PD classes (defined as new classes not present in the training data), models will misclassify unknown classes as one of the known classes. To address this, a novel hybrid open-set recognition (OSR) framework based on Supervised Contrastive Learning (SupCon) is proposed to address a previously unexplored direction in the PD diagnosis domain. The framework integrates discriminative representation learning with both unified and per-class rejection strategies using one-class classification, enabling effective separation of known and unknown PD classes. The main contribution is the synergistic integration of SupCon for constructing structured latent spaces, SVM for precise closed-set classification, and dual OCSVMs for adaptive unknown rejection, together forming a unified pipeline that achieves both fine-grained discrimination and robust unknown detection. To evaluate the effectiveness of the proposed framework, comprehensive experiments are conducted across 30 OSR tasks, covering 12 PD classes from three types of high-voltage equipment under varying openness levels. The proposed framework is benchmarked against five state-of-the-art approaches, including ArcFace, GAN-Flow, a convolutional neural network (CNN), Autoencoder, and Vision Transformer. Experimental results demonstrate that the proposed framework achieved the best performance, with a mean normalized accuracy of 97.66 % and a Youden’s index of 0.953, confirming its robustness, generalization capability, and potential to advance open-set PD diagnostic methodologies.</div></div>\",\"PeriodicalId\":48648,\"journal\":{\"name\":\"Ain Shams Engineering Journal\",\"volume\":\"16 12\",\"pages\":\"Article 103762\"},\"PeriodicalIF\":5.9000,\"publicationDate\":\"2025-09-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ain Shams Engineering Journal\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2090447925005039\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENGINEERING, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ain Shams Engineering Journal","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2090447925005039","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

摘要

利用机器学习模型进行局部放电(PD)自动诊断对高压设备(HVE)绝缘状态监测非常有用。然而,如果没有一种机制来识别未知的PD类(定义为未出现在训练数据中的新类),模型就会将未知类错误地分类为已知类之一。为了解决这个问题,提出了一种基于监督对比学习(SupCon)的新型混合开放集识别(OSR)框架,以解决PD诊断领域以前未探索的方向。该框架将判别表示学习与使用单类分类的统一拒绝策略和单类拒绝策略相结合,实现了已知和未知PD类的有效分离。其主要贡献是SupCon(用于构造结构化潜在空间)、SVM(用于精确闭集分类)和双ocsvm(用于自适应未知拒绝)的协同集成,共同形成了一个统一的管道,实现了细粒度识别和鲁棒未知检测。为了评估所提出的框架的有效性,在30个OSR任务中进行了综合实验,涵盖了三种高压设备在不同开放水平下的12个PD类别。提出的框架是针对五种最先进的方法进行基准测试的,包括ArcFace、GAN-Flow、卷积神经网络(CNN)、自动编码器和视觉变压器。实验结果表明,所提出的框架达到了最佳性能,平均归一化准确率为97.66%,约登指数为0.953,证实了其鲁棒性、泛化能力和推进开集PD诊断方法的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Robust open-set partial discharge diagnosis based on hybrid supervised contrastive learning and SVM framework
Automated partial discharge (PD) diagnosis using machine learning models is useful for high-voltage equipment (HVE) insulation condition monitoring. However, without a mechanism to identify unknown PD classes (defined as new classes not present in the training data), models will misclassify unknown classes as one of the known classes. To address this, a novel hybrid open-set recognition (OSR) framework based on Supervised Contrastive Learning (SupCon) is proposed to address a previously unexplored direction in the PD diagnosis domain. The framework integrates discriminative representation learning with both unified and per-class rejection strategies using one-class classification, enabling effective separation of known and unknown PD classes. The main contribution is the synergistic integration of SupCon for constructing structured latent spaces, SVM for precise closed-set classification, and dual OCSVMs for adaptive unknown rejection, together forming a unified pipeline that achieves both fine-grained discrimination and robust unknown detection. To evaluate the effectiveness of the proposed framework, comprehensive experiments are conducted across 30 OSR tasks, covering 12 PD classes from three types of high-voltage equipment under varying openness levels. The proposed framework is benchmarked against five state-of-the-art approaches, including ArcFace, GAN-Flow, a convolutional neural network (CNN), Autoencoder, and Vision Transformer. Experimental results demonstrate that the proposed framework achieved the best performance, with a mean normalized accuracy of 97.66 % and a Youden’s index of 0.953, confirming its robustness, generalization capability, and potential to advance open-set PD diagnostic methodologies.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Ain Shams Engineering Journal
Ain Shams Engineering Journal Engineering-General Engineering
CiteScore
10.80
自引率
13.30%
发文量
441
审稿时长
49 weeks
期刊介绍: in Shams Engineering Journal is an international journal devoted to publication of peer reviewed original high-quality research papers and review papers in both traditional topics and those of emerging science and technology. Areas of both theoretical and fundamental interest as well as those concerning industrial applications, emerging instrumental techniques and those which have some practical application to an aspect of human endeavor, such as the preservation of the environment, health, waste disposal are welcome. The overall focus is on original and rigorous scientific research results which have generic significance. Ain Shams Engineering Journal focuses upon aspects of mechanical engineering, electrical engineering, civil engineering, chemical engineering, petroleum engineering, environmental engineering, architectural and urban planning engineering. Papers in which knowledge from other disciplines is integrated with engineering are especially welcome like nanotechnology, material sciences, and computational methods as well as applied basic sciences: engineering mathematics, physics and chemistry.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信