Machine fault diagnosis based on multi-head deep learning network

Qidong Lu, Yu Qin, Yingying Li, Zhiliang Qin, Xiaowei Liu
{"title":"Machine fault diagnosis based on multi-head deep learning network","authors":"Qidong Lu, Yu Qin, Yingying Li, Zhiliang Qin, Xiaowei Liu","doi":"10.1117/12.2581262","DOIUrl":null,"url":null,"abstract":"Feature extraction and utilization is of great importance for the problem of machine fault diagnosis. In this paper, multihead deep learning network is proposed to achieve machine health status classification using features of different sizes. Firstly, statistical characteristics which reflect machine signal status of time domain and frequency domain are summarized to compose feature vectors as one-dimensional network input. Secondly, Mel power spectrum and its incremental characteristics are utilized as two-dimensional network input of three channels. Lastly, the multi-head network is introduced to analyze both one-dimensional and two-dimensional features using two different sub neural networks and classify the machine health status according to the joint feature analyzing result. The experiments on bearing working status database of Case Western Reserve University show that the proposed method has good mechanical signal classification ability and better stability. Moreover, our final test accuracy of fault diagnosis on 16 kinds of bearing working signals can reach up to about 99.53%.","PeriodicalId":415097,"journal":{"name":"International Conference on Signal Processing Systems","volume":"13 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Conference on Signal Processing Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1117/12.2581262","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

Feature extraction and utilization is of great importance for the problem of machine fault diagnosis. In this paper, multihead deep learning network is proposed to achieve machine health status classification using features of different sizes. Firstly, statistical characteristics which reflect machine signal status of time domain and frequency domain are summarized to compose feature vectors as one-dimensional network input. Secondly, Mel power spectrum and its incremental characteristics are utilized as two-dimensional network input of three channels. Lastly, the multi-head network is introduced to analyze both one-dimensional and two-dimensional features using two different sub neural networks and classify the machine health status according to the joint feature analyzing result. The experiments on bearing working status database of Case Western Reserve University show that the proposed method has good mechanical signal classification ability and better stability. Moreover, our final test accuracy of fault diagnosis on 16 kinds of bearing working signals can reach up to about 99.53%.
基于多头深度学习网络的机器故障诊断
特征的提取与利用对机械故障诊断具有重要意义。本文提出了多头深度学习网络,利用不同大小的特征实现机器健康状态分类。首先,总结反映机器信号时域和频域状态的统计特征,组成特征向量作为一维网络输入;其次,利用Mel功率谱及其增量特性作为三通道二维网络输入;最后,引入多头网络,利用两个不同的子神经网络对机器的一维和二维特征进行分析,并根据联合特征分析结果对机器的健康状态进行分类。在美国凯斯西储大学轴承工作状态数据库上的实验表明,该方法具有良好的机械信号分类能力和较好的稳定性。对16种轴承工作信号进行故障诊断的最终测试精度可达99.53%左右。
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