基于多传感器的动车组换气装置故障检测方法

IF 2.2 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Rongjiao Wei;Pu Wei;Chao Pan;Yangyang An;Hao Zhu;Mulan Wang
{"title":"基于多传感器的动车组换气装置故障检测方法","authors":"Rongjiao Wei;Pu Wei;Chao Pan;Yangyang An;Hao Zhu;Mulan Wang","doi":"10.1109/LSENS.2025.3558957","DOIUrl":null,"url":null,"abstract":"In this letter, we introduce and experimentally demonstrate a fault detection method for the air-exchange devices in the electric multiple unit (EMU) train, which utilizes the abnormal sound and vibration generated by the devices when the faults occur. The sound and vibration signals are fused, and the time–frequency matrix is extracted using a short-time Fourier transform (STFT). Fault recognition is performed using the trained support vector machine (SVM) classifier. A sound detection system is built for experiments, in which the package of the sound sensor is designed to shield the sound from adjacent devices. The system includes an field programmable gate array (FPGA) and an embedded system and can be used for fault detection in the future. The experiment shows that the accuracy of the fused signals is higher than the single sensor, up to 0.995. In addition, the performances of the algorithm are evaluated, and the precision, recall, accuracy, and F1-score are all up to 0.99, which meet the actual fault detection requirements. Our method effectively improves the efficiency and accuracy of fault detection for the air-exchange device and can be widely used in the EMU train.","PeriodicalId":13014,"journal":{"name":"IEEE Sensors Letters","volume":"9 5","pages":"1-4"},"PeriodicalIF":2.2000,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Multisensor-Based Fault Detection Method for Air-Exchange Device in EMU Train\",\"authors\":\"Rongjiao Wei;Pu Wei;Chao Pan;Yangyang An;Hao Zhu;Mulan Wang\",\"doi\":\"10.1109/LSENS.2025.3558957\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this letter, we introduce and experimentally demonstrate a fault detection method for the air-exchange devices in the electric multiple unit (EMU) train, which utilizes the abnormal sound and vibration generated by the devices when the faults occur. The sound and vibration signals are fused, and the time–frequency matrix is extracted using a short-time Fourier transform (STFT). Fault recognition is performed using the trained support vector machine (SVM) classifier. A sound detection system is built for experiments, in which the package of the sound sensor is designed to shield the sound from adjacent devices. The system includes an field programmable gate array (FPGA) and an embedded system and can be used for fault detection in the future. The experiment shows that the accuracy of the fused signals is higher than the single sensor, up to 0.995. In addition, the performances of the algorithm are evaluated, and the precision, recall, accuracy, and F1-score are all up to 0.99, which meet the actual fault detection requirements. Our method effectively improves the efficiency and accuracy of fault detection for the air-exchange device and can be widely used in the EMU train.\",\"PeriodicalId\":13014,\"journal\":{\"name\":\"IEEE Sensors Letters\",\"volume\":\"9 5\",\"pages\":\"1-4\"},\"PeriodicalIF\":2.2000,\"publicationDate\":\"2025-04-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE Sensors Letters\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10956167/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, ELECTRICAL & ELECTRONIC\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Sensors Letters","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10956167/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0

摘要

本文介绍并实验证明了一种利用故障发生时换气装置产生的异常声音和振动对动车组换气装置进行故障检测的方法。采用短时傅里叶变换(STFT)对声、振动信号进行融合,提取时频矩阵。使用训练好的支持向量机(SVM)分类器进行故障识别。建立了一种实验用的声音检测系统,在该系统中,声音传感器的封装被设计成屏蔽邻近设备的声音。该系统包括一个现场可编程门阵列(FPGA)和一个嵌入式系统,可用于未来的故障检测。实验表明,融合后的信号精度高于单个传感器,可达0.995。此外,对算法的性能进行了评价,精密度、查全率、正确率和f1得分均达到0.99,满足实际故障检测的要求。该方法有效地提高了换气装置故障检测的效率和准确性,可广泛应用于动车组列车。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multisensor-Based Fault Detection Method for Air-Exchange Device in EMU Train
In this letter, we introduce and experimentally demonstrate a fault detection method for the air-exchange devices in the electric multiple unit (EMU) train, which utilizes the abnormal sound and vibration generated by the devices when the faults occur. The sound and vibration signals are fused, and the time–frequency matrix is extracted using a short-time Fourier transform (STFT). Fault recognition is performed using the trained support vector machine (SVM) classifier. A sound detection system is built for experiments, in which the package of the sound sensor is designed to shield the sound from adjacent devices. The system includes an field programmable gate array (FPGA) and an embedded system and can be used for fault detection in the future. The experiment shows that the accuracy of the fused signals is higher than the single sensor, up to 0.995. In addition, the performances of the algorithm are evaluated, and the precision, recall, accuracy, and F1-score are all up to 0.99, which meet the actual fault detection requirements. Our method effectively improves the efficiency and accuracy of fault detection for the air-exchange device and can be widely used in the EMU train.
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
IEEE Sensors Letters
IEEE Sensors Letters Engineering-Electrical and Electronic Engineering
CiteScore
3.50
自引率
7.10%
发文量
194
×
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