利用深度学习和振动分析优化离合器系统故障诊断的样本长度

IF 2.3 4区 工程技术 Q2 ENGINEERING, MECHANICAL
Ganjikunta Chakrapani, Sridharan Naveen Venkatesh, Tapan Kumar Mahanta, Natrayan Lakshmaiya, Vaithiyanathan Sugumaran
{"title":"利用深度学习和振动分析优化离合器系统故障诊断的样本长度","authors":"Ganjikunta Chakrapani, Sridharan Naveen Venkatesh, Tapan Kumar Mahanta, Natrayan Lakshmaiya, Vaithiyanathan Sugumaran","doi":"10.1177/09544089241272791","DOIUrl":null,"url":null,"abstract":"Clutches are prone to failure owing to extended heat exposure and high levels of abrasion during power transfer. Internal damage, downtime, and permanent transmission system lock-up all can result from these faults. To detect and diagnose these faults, this study employs the deep learning approach. Vibration signals were obtained from a test rig that was exposed to various clutch conditions at various loads. The amount of data points (signal length) when collecting vibration signals from a test rig can have a significant effect on the accuracy of results. A shorter sample length can lead to an increased uncertainty in the results, while a longer sample length can lead to more accurate results. A longer sample length also increases the computational complexity of the diagnosis process, which can lead to longer execution times. In this study vibration signals were collected for various sample lengths to find the optimal sample length for systemic clutch fault diagnostics. The collected vibration signals are analyzed and transformed into vibration plots that serve as input to the deep learning pretrained network. VGG-16 model was considered for this study to diagnose the clutch system faults. Based on the outcomes, the optimal sample length for the no load condition was identified as 4000, while for the 5-kg load and 10-kg load conditions 3000 sample length was suggested for fault diagnosis of the clutch system.","PeriodicalId":20552,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimizing sample length for fault diagnosis of clutch systems using deep learning and vibration analysis\",\"authors\":\"Ganjikunta Chakrapani, Sridharan Naveen Venkatesh, Tapan Kumar Mahanta, Natrayan Lakshmaiya, Vaithiyanathan Sugumaran\",\"doi\":\"10.1177/09544089241272791\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Clutches are prone to failure owing to extended heat exposure and high levels of abrasion during power transfer. Internal damage, downtime, and permanent transmission system lock-up all can result from these faults. To detect and diagnose these faults, this study employs the deep learning approach. Vibration signals were obtained from a test rig that was exposed to various clutch conditions at various loads. The amount of data points (signal length) when collecting vibration signals from a test rig can have a significant effect on the accuracy of results. A shorter sample length can lead to an increased uncertainty in the results, while a longer sample length can lead to more accurate results. A longer sample length also increases the computational complexity of the diagnosis process, which can lead to longer execution times. In this study vibration signals were collected for various sample lengths to find the optimal sample length for systemic clutch fault diagnostics. The collected vibration signals are analyzed and transformed into vibration plots that serve as input to the deep learning pretrained network. VGG-16 model was considered for this study to diagnose the clutch system faults. Based on the outcomes, the optimal sample length for the no load condition was identified as 4000, while for the 5-kg load and 10-kg load conditions 3000 sample length was suggested for fault diagnosis of the clutch system.\",\"PeriodicalId\":20552,\"journal\":{\"name\":\"Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2024-08-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1177/09544089241272791\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1177/09544089241272791","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
引用次数: 0

摘要

离合器在动力传输过程中由于长时间受热和高度磨损而容易发生故障。这些故障可能导致内部损坏、停机和永久性传动系统锁定。为了检测和诊断这些故障,本研究采用了深度学习方法。振动信号来自一个试验台,该试验台在不同负载下暴露在各种离合器条件下。从试验台架上采集振动信号时,数据点的数量(信号长度)会对结果的准确性产生重大影响。样本长度越短,结果的不确定性就越大,而样本长度越长,结果就越准确。较长的样本长度还会增加诊断过程的计算复杂性,导致执行时间延长。本研究收集了不同采样长度的振动信号,以找到系统离合器故障诊断的最佳采样长度。收集到的振动信号经过分析后转化为振动图,作为深度学习预训练网络的输入。本研究考虑使用 VGG-16 模型来诊断离合器系统故障。根据研究结果,确定空载条件下的最佳样本长度为 4000,而 5 千克负载和 10 千克负载条件下的最佳样本长度为 3000,用于离合器系统的故障诊断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimizing sample length for fault diagnosis of clutch systems using deep learning and vibration analysis
Clutches are prone to failure owing to extended heat exposure and high levels of abrasion during power transfer. Internal damage, downtime, and permanent transmission system lock-up all can result from these faults. To detect and diagnose these faults, this study employs the deep learning approach. Vibration signals were obtained from a test rig that was exposed to various clutch conditions at various loads. The amount of data points (signal length) when collecting vibration signals from a test rig can have a significant effect on the accuracy of results. A shorter sample length can lead to an increased uncertainty in the results, while a longer sample length can lead to more accurate results. A longer sample length also increases the computational complexity of the diagnosis process, which can lead to longer execution times. In this study vibration signals were collected for various sample lengths to find the optimal sample length for systemic clutch fault diagnostics. The collected vibration signals are analyzed and transformed into vibration plots that serve as input to the deep learning pretrained network. VGG-16 model was considered for this study to diagnose the clutch system faults. Based on the outcomes, the optimal sample length for the no load condition was identified as 4000, while for the 5-kg load and 10-kg load conditions 3000 sample length was suggested for fault diagnosis of the clutch system.
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
CiteScore
3.80
自引率
16.70%
发文量
370
审稿时长
6 months
期刊介绍: The Journal of Process Mechanical Engineering publishes high-quality, peer-reviewed papers covering a broad area of mechanical engineering activities associated with the design and operation of process equipment.
×
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