A fault diagnosis method based on dilated convolution and attention for rolling bearing under multiple working conditions and noisy environments

IF 0.7 Q4 ENGINEERING, MECHANICAL Journal of Vibroengineering Pub Date : 2023-08-01 DOI:10.21595/jve.2023.23326
Hui Zhang, Shengdong Liu, Ziwei Lv, Zhenlong Sang, Fang Li
{"title":"A fault diagnosis method based on dilated convolution and attention for rolling bearing under multiple working conditions and noisy environments","authors":"Hui Zhang, Shengdong Liu, Ziwei Lv, Zhenlong Sang, Fang Li","doi":"10.21595/jve.2023.23326","DOIUrl":null,"url":null,"abstract":"As essential equipment in rotating machinery, the fault diagnosis technology of rolling bearings has achieved great success. However, it still suffers from limitations in terms of generalization and noise resistance performance when operating under complex conditions. To accurately identify the fault types of rolling bearings under different loads and nosy environments, a novel intelligent fault diagnosis method is proposed. Firstly, the utilization of dilated convolution expands the network's receptive field, thereby effectively enhancing the scope of fault extraction. Then, by incorporating the Efficient Channel Attention (ECA) in different convolutional layers, the extracted features are adaptively recognized, highlighting important representation information and improving fault diagnosis performance. Finally, the proposed network is utilized for rolling bearing fault diagnosis under diverse operating and noise conditions, and its efficacy is evaluated on various datasets. The experimental results demonstrate that the proposed method exhibits good generalization performance and strong robustness, compared with other methods.","PeriodicalId":49956,"journal":{"name":"Journal of Vibroengineering","volume":null,"pages":null},"PeriodicalIF":0.7000,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Vibroengineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.21595/jve.2023.23326","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
引用次数: 0

Abstract

As essential equipment in rotating machinery, the fault diagnosis technology of rolling bearings has achieved great success. However, it still suffers from limitations in terms of generalization and noise resistance performance when operating under complex conditions. To accurately identify the fault types of rolling bearings under different loads and nosy environments, a novel intelligent fault diagnosis method is proposed. Firstly, the utilization of dilated convolution expands the network's receptive field, thereby effectively enhancing the scope of fault extraction. Then, by incorporating the Efficient Channel Attention (ECA) in different convolutional layers, the extracted features are adaptively recognized, highlighting important representation information and improving fault diagnosis performance. Finally, the proposed network is utilized for rolling bearing fault diagnosis under diverse operating and noise conditions, and its efficacy is evaluated on various datasets. The experimental results demonstrate that the proposed method exhibits good generalization performance and strong robustness, compared with other methods.
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
一种基于扩张卷积和注意力的滚动轴承多工况噪声环境故障诊断方法
滚动轴承作为旋转机械的重要设备,其故障诊断技术取得了巨大的成功。然而,当在复杂条件下操作时,它在泛化和抗噪声性能方面仍然受到限制。为了准确识别滚动轴承在不同载荷和恶劣环境下的故障类型,提出了一种新的智能故障诊断方法。首先,利用扩张卷积扩大了网络的感受野,从而有效地扩大了故障提取的范围。然后,通过在不同的卷积层中引入有效通道注意(ECA),自适应地识别提取的特征,突出重要的表示信息,提高故障诊断性能。最后,将所提出的网络用于不同运行和噪声条件下的滚动轴承故障诊断,并在各种数据集上评估其有效性。实验结果表明,与其他方法相比,该方法具有良好的泛化性能和较强的鲁棒性。
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 去求助
来源期刊
Journal of Vibroengineering
Journal of Vibroengineering 工程技术-工程:机械
CiteScore
1.70
自引率
0.00%
发文量
97
审稿时长
4.5 months
期刊介绍: Journal of VIBROENGINEERING (JVE) ISSN 1392-8716 is a prestigious peer reviewed International Journal specializing in theoretical and practical aspects of Vibration Engineering. It is indexed in ESCI and other major databases. Published every 1.5 months (8 times yearly), the journal attracts attention from the International Engineering Community.
期刊最新文献
Effect of AVL-based time-domain analysis on torsional vibration of engine shafting Seismic performance of beam-type covered bridge considering the superstructure – substructure interaction and bearing mechanical property Fault diagnosis algorithm based on GADF-DFT and multi-kernel domain coordinated adaptive network A novel cross-domain identification method for bridge damage based on recurrence plot and convolutional neural networks Study on the mechanical characteristics and impact resistance improvement of substation masonry wall under flood load
×
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