基于数据缺失和特征转移抑制策略的轴承故障诊断

Yunji Zhao, Jun Xu
{"title":"基于数据缺失和特征转移抑制策略的轴承故障诊断","authors":"Yunji Zhao, Jun Xu","doi":"10.1177/09596518241237080","DOIUrl":null,"url":null,"abstract":"To mitigate the impact of fault iconic feature shift and feature missing due to missing data values on bearing fault diagnosis, this paper proposes a fault diagnosis method based on a spatial frequency filter and a Multi-Scale feature recombination calibration network (MSRCN). First, the fault features are converted into frequency band features and feature enhancement is realized using Mel filters to weaken the effect of fault feature offset. Then, the spatial calibration module (SC) in the MSRCN is utilized to further improve the fault feature distribution and eliminate the fault feature offset problem. Next, to solve the fault feature missing problem, the remaining fault features are sampled by multi-scale reorganization using MSRCN to obtain new fault features, which overcomes the effect of fault feature missing on fault diagnosis. Finally, experiments are conducted on CWRU and XJTU-SY rolling bearing datasets to verify that the algorithm can effectively solve the fault feature offset and missing problem. Meanwhile, the experimental results prove that the algorithm proposed in this paper can realize high-precision fault diagnosis.","PeriodicalId":20638,"journal":{"name":"Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering","volume":"101 1","pages":""},"PeriodicalIF":1.4000,"publicationDate":"2024-04-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Bearing fault diagnosis based on data missing and feature shift suppression strategy\",\"authors\":\"Yunji Zhao, Jun Xu\",\"doi\":\"10.1177/09596518241237080\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"To mitigate the impact of fault iconic feature shift and feature missing due to missing data values on bearing fault diagnosis, this paper proposes a fault diagnosis method based on a spatial frequency filter and a Multi-Scale feature recombination calibration network (MSRCN). First, the fault features are converted into frequency band features and feature enhancement is realized using Mel filters to weaken the effect of fault feature offset. Then, the spatial calibration module (SC) in the MSRCN is utilized to further improve the fault feature distribution and eliminate the fault feature offset problem. Next, to solve the fault feature missing problem, the remaining fault features are sampled by multi-scale reorganization using MSRCN to obtain new fault features, which overcomes the effect of fault feature missing on fault diagnosis. Finally, experiments are conducted on CWRU and XJTU-SY rolling bearing datasets to verify that the algorithm can effectively solve the fault feature offset and missing problem. Meanwhile, the experimental results prove that the algorithm proposed in this paper can realize high-precision fault diagnosis.\",\"PeriodicalId\":20638,\"journal\":{\"name\":\"Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering\",\"volume\":\"101 1\",\"pages\":\"\"},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2024-04-06\",\"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 I: Journal of Systems and Control Engineering\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1177/09596518241237080\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1177/09596518241237080","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

为减轻故障图标特征偏移和数据值缺失导致的特征缺失对轴承故障诊断的影响,本文提出了一种基于空间频率滤波器和多尺度特征重组校准网络(MSRCN)的故障诊断方法。首先,将故障特征转换为频带特征,并利用 Mel 滤波器实现特征增强,以削弱故障特征偏移的影响。然后,利用 MSRCN 中的空间校准模块(SC)进一步改善故障特征分布,消除故障特征偏移问题。接下来,为了解决故障特征缺失问题,利用 MSRCN 对剩余的故障特征进行多尺度重组采样,得到新的故障特征,从而克服了故障特征缺失对故障诊断的影响。最后,在 CWRU 和 XJTU-SY 滚动轴承数据集上进行了实验,验证了该算法能有效解决故障特征偏移和缺失问题。同时,实验结果证明本文提出的算法可以实现高精度故障诊断。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Bearing fault diagnosis based on data missing and feature shift suppression strategy
To mitigate the impact of fault iconic feature shift and feature missing due to missing data values on bearing fault diagnosis, this paper proposes a fault diagnosis method based on a spatial frequency filter and a Multi-Scale feature recombination calibration network (MSRCN). First, the fault features are converted into frequency band features and feature enhancement is realized using Mel filters to weaken the effect of fault feature offset. Then, the spatial calibration module (SC) in the MSRCN is utilized to further improve the fault feature distribution and eliminate the fault feature offset problem. Next, to solve the fault feature missing problem, the remaining fault features are sampled by multi-scale reorganization using MSRCN to obtain new fault features, which overcomes the effect of fault feature missing on fault diagnosis. Finally, experiments are conducted on CWRU and XJTU-SY rolling bearing datasets to verify that the algorithm can effectively solve the fault feature offset and missing problem. Meanwhile, the experimental results prove that the algorithm proposed in this paper can realize high-precision fault diagnosis.
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
3.50
自引率
18.80%
发文量
99
审稿时长
4.2 months
期刊介绍: Systems and control studies provide a unifying framework for a wide range of engineering disciplines and industrial applications. The Journal of Systems and Control Engineering refleSystems and control studies provide a unifying framework for a wide range of engineering disciplines and industrial applications. The Journal of Systems and Control Engineering reflects this diversity by giving prominence to experimental application and industrial studies. "It is clear from the feedback we receive that the Journal is now recognised as one of the leaders in its field. We are particularly interested in highlighting experimental applications and industrial studies, but also new theoretical developments which are likely to provide the foundation for future applications. In 2009, we launched a new Series of "Forward Look" papers written by leading researchers and practitioners. These short articles are intended to be provocative and help to set the agenda for future developments. We continue to strive for fast decision times and minimum delays in the production processes." Professor Cliff Burrows - University of Bath, UK This journal is a member of the Committee on Publication Ethics (COPE).cts this diversity by giving prominence to experimental application and industrial studies.
期刊最新文献
Hybrid-triggered H∞ control for Markov jump systems with quantizations and hybrid attacks Design optimization and simulation of a 3D printed cable-driven continuum robot using IKM-ANN and nTop software Optimal course tracking control of USV with input dead zone based on adaptive fuzzy dynamic programing Development of new framework for order abatement and control design strategy Unwinding-free composite full-order sliding-mode control for attitude tracking of flexible spacecraft
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1