用于电气设备诊断的替代模型

Q3 Engineering

Russian Electrical Engineering Pub Date : 2024-01-30 DOI:10.3103/s1068371223110056

G. A. Kilin, B. V. Kavalerov, A. I. Suslov, D. A. Shcherbinin

{"title":"用于电气设备诊断的替代模型","authors":"G. A. Kilin, B. V. Kavalerov, A. I. Suslov, D. A. Shcherbinin","doi":"10.3103/s1068371223110056","DOIUrl":null,"url":null,"abstract":"<h3 data-test=\"abstract-sub-heading\">Abstract</h3><p>The possibility of using modern contactless methods of diagnostics and control of electrical equipment is considered. One such method is the use of artificial neural networks to assess the current state of electrical equipment, which allow timely detection of various malfunctions, assess its condition and prevent possible failures.</p>","PeriodicalId":39312,"journal":{"name":"Russian Electrical Engineering","volume":"299 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Surrogate Models for Diagnostics of Electrical Equipment\",\"authors\":\"G. A. Kilin, B. V. Kavalerov, A. I. Suslov, D. A. Shcherbinin\",\"doi\":\"10.3103/s1068371223110056\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<h3 data-test=\\\"abstract-sub-heading\\\">Abstract</h3><p>The possibility of using modern contactless methods of diagnostics and control of electrical equipment is considered. One such method is the use of artificial neural networks to assess the current state of electrical equipment, which allow timely detection of various malfunctions, assess its condition and prevent possible failures.</p>\",\"PeriodicalId\":39312,\"journal\":{\"name\":\"Russian Electrical Engineering\",\"volume\":\"299 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-01-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Russian Electrical Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3103/s1068371223110056\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Russian Electrical Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3103/s1068371223110056","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Engineering","Score":null,"Total":0}

引用次数: 0

摘要

摘要考虑了使用现代非接触方法诊断和控制电气设备的可能性。其中一种方法是使用人工神经网络来评估电气设备的当前状态，从而及时发现各种故障、评估其状况并预防可能出现的故障。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

Surrogate Models for Diagnostics of Electrical Equipment

查看原文本刊更多论文

Surrogate Models for Diagnostics of Electrical Equipment

Abstract

The possibility of using modern contactless methods of diagnostics and control of electrical equipment is considered. One such method is the use of artificial neural networks to assess the current state of electrical equipment, which allow timely detection of various malfunctions, assess its condition and prevent possible failures.

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

Russian Electrical Engineering Engineering-Electrical and Electronic Engineering

CiteScore

1.50

自引率

0.00%

发文量

期刊介绍： Russian Electrical Engineering is a journal designed for the electrical engineering industry and publishes the latest research results on the design and utilization of new types of equipment for that industry and on the ways of improving the efficiency of existing equipment.