无数据的电子电路诊断

Varun Khemani, M. Azarian, M. Pecht
{"title":"无数据的电子电路诊断","authors":"Varun Khemani, M. Azarian, M. Pecht","doi":"10.1109/ICPHM.2019.8819424","DOIUrl":null,"url":null,"abstract":"Operational data from the target system is widely considered a pre-requisite for implementation of PHM, as it used as training data. Often this data is not available to PHM practitioners because health monitoring capabilities may not be installed in legacy systems. This research presents an approach in which fault diagnosis can be implemented without any operational data and is generic enough to be applied to any electronic circuit provided a simulation model of the system with acceptable fidelity can be developed. The research also employs the Space-Filling Design, which can be used to generate the training data in a systematic, statistically valid framework, and is especially valuable for complex circuit with a large number of components. This design provides sufficient coverage of the parametric design space to be representative of the unavailable operational data, as well as incorporating the effects of parameter interaction on the simulated response of the system. Most PHM studies in the literature ignore the effect of the degradation of interacting components. We show, how such an assumption can lead to incorrect fault diagnosis/RUL estimation and propose methods to screen for two-way and higher order interactions. Finally, we use various deep learning approaches to diagnose circuit faults. This simulation-based fusion approach is a holistic framework for all types of analog electronic circuits.","PeriodicalId":113460,"journal":{"name":"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"4","resultStr":"{\"title\":\"Electronic Circuit Diagnosis with No Data\",\"authors\":\"Varun Khemani, M. Azarian, M. Pecht\",\"doi\":\"10.1109/ICPHM.2019.8819424\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Operational data from the target system is widely considered a pre-requisite for implementation of PHM, as it used as training data. Often this data is not available to PHM practitioners because health monitoring capabilities may not be installed in legacy systems. This research presents an approach in which fault diagnosis can be implemented without any operational data and is generic enough to be applied to any electronic circuit provided a simulation model of the system with acceptable fidelity can be developed. The research also employs the Space-Filling Design, which can be used to generate the training data in a systematic, statistically valid framework, and is especially valuable for complex circuit with a large number of components. This design provides sufficient coverage of the parametric design space to be representative of the unavailable operational data, as well as incorporating the effects of parameter interaction on the simulated response of the system. Most PHM studies in the literature ignore the effect of the degradation of interacting components. We show, how such an assumption can lead to incorrect fault diagnosis/RUL estimation and propose methods to screen for two-way and higher order interactions. Finally, we use various deep learning approaches to diagnose circuit faults. This simulation-based fusion approach is a holistic framework for all types of analog electronic circuits.\",\"PeriodicalId\":113460,\"journal\":{\"name\":\"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)\",\"volume\":\"59 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-06-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"4\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICPHM.2019.8819424\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE International Conference on Prognostics and Health Management (ICPHM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICPHM.2019.8819424","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 4

摘要

来自目标系统的操作数据被广泛认为是PHM实施的先决条件,因为它被用作训练数据。PHM从业者通常无法获得这些数据,因为遗留系统中可能没有安装健康监测功能。本研究提出了一种无需任何运行数据即可实现故障诊断的方法,该方法具有足够的通用性,可以应用于任何电子电路,只要可以开发出具有可接受保真度的系统仿真模型。该研究还采用了空间填充设计,可以在一个系统的、统计有效的框架中生成训练数据,对于具有大量组件的复杂电路特别有价值。该设计提供了足够的参数化设计空间,以代表不可用的操作数据,并结合参数交互对系统模拟响应的影响。文献中的大多数PHM研究忽略了相互作用组分降解的影响。我们展示了这样的假设如何导致错误的故障诊断/RUL估计,并提出了筛选双向和高阶交互的方法。最后,我们使用各种深度学习方法来诊断电路故障。这种基于仿真的融合方法是一种适用于所有类型模拟电子电路的整体框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Electronic Circuit Diagnosis with No Data
Operational data from the target system is widely considered a pre-requisite for implementation of PHM, as it used as training data. Often this data is not available to PHM practitioners because health monitoring capabilities may not be installed in legacy systems. This research presents an approach in which fault diagnosis can be implemented without any operational data and is generic enough to be applied to any electronic circuit provided a simulation model of the system with acceptable fidelity can be developed. The research also employs the Space-Filling Design, which can be used to generate the training data in a systematic, statistically valid framework, and is especially valuable for complex circuit with a large number of components. This design provides sufficient coverage of the parametric design space to be representative of the unavailable operational data, as well as incorporating the effects of parameter interaction on the simulated response of the system. Most PHM studies in the literature ignore the effect of the degradation of interacting components. We show, how such an assumption can lead to incorrect fault diagnosis/RUL estimation and propose methods to screen for two-way and higher order interactions. Finally, we use various deep learning approaches to diagnose circuit faults. This simulation-based fusion approach is a holistic framework for all types of analog electronic circuits.
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
自引率
0.00%
发文量
0
×
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:481959085
Book学术官方微信