Aircraft sensor Fault Diagnosis Method Based on Residual Antagonism Transfer Learning

Xiaoya Li, Xiangwei Kong
{"title":"Aircraft sensor Fault Diagnosis Method Based on Residual Antagonism Transfer Learning","authors":"Xiaoya Li, Xiangwei Kong","doi":"10.1109/AIID51893.2021.9456530","DOIUrl":null,"url":null,"abstract":"With the rapid development of electronic information technology, aircraft has entered a completely electrified era, and the number of sensors has increased exponentially. Although key sensors have redundant designs, many aviation accidents in recent years are caused by sensor failures. Therefore, early detection of Aircraft sensor faults is of great significance for ensuring flight safety. Faced with a large number of unlabeled and uneven sample sensor data, a method for fault diagnosis of Aircraft sensors based on residual countermeasure migration learning is proposed. This method can help deep learning. The product neural network requires the limitation of a large number of labeled data, and uses the rich label data from different but related auxiliary fields to reuse and transfer the data of the target domain to achieve the purpose of transfer learning and realize the fault diagnosis of Aircraft sensors.","PeriodicalId":412698,"journal":{"name":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE International Conference on Artificial Intelligence and Industrial Design (AIID)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/AIID51893.2021.9456530","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 5

Abstract

With the rapid development of electronic information technology, aircraft has entered a completely electrified era, and the number of sensors has increased exponentially. Although key sensors have redundant designs, many aviation accidents in recent years are caused by sensor failures. Therefore, early detection of Aircraft sensor faults is of great significance for ensuring flight safety. Faced with a large number of unlabeled and uneven sample sensor data, a method for fault diagnosis of Aircraft sensors based on residual countermeasure migration learning is proposed. This method can help deep learning. The product neural network requires the limitation of a large number of labeled data, and uses the rich label data from different but related auxiliary fields to reuse and transfer the data of the target domain to achieve the purpose of transfer learning and realize the fault diagnosis of Aircraft sensors.
基于残差对抗迁移学习的飞机传感器故障诊断方法
随着电子信息技术的飞速发展,飞机进入了完全电气化时代,传感器数量呈指数级增长。虽然关键传感器具有冗余设计,但近年来许多航空事故都是由传感器故障引起的。因此,及早发现飞机传感器故障对保证飞行安全具有重要意义。针对大量未标记且不均匀的传感器样本数据,提出了一种基于残差对策迁移学习的飞机传感器故障诊断方法。这种方法可以帮助深度学习。产品神经网络需要大量标记数据的限制,利用来自不同但相关的辅助领域的丰富标记数据,对目标域的数据进行重用和传递,以达到迁移学习的目的,实现飞机传感器的故障诊断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
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:604180095
Book学术官方微信