Time-frequency analysis-based impulse feature extraction method for quantitative evaluation of milling tool wear

IF 5.7 2区 工程技术 Q1 ENGINEERING, MULTIDISCIPLINARY
MingAng Guo, Xiaotong Tu, Saqlain Abbas, Shuangmu Zhuo, Xiaolu Li
{"title":"Time-frequency analysis-based impulse feature extraction method for quantitative evaluation of milling tool wear","authors":"MingAng Guo, Xiaotong Tu, Saqlain Abbas, Shuangmu Zhuo, Xiaolu Li","doi":"10.1177/14759217231192003","DOIUrl":null,"url":null,"abstract":"Mechanical system condition monitoring is an important procedure in modern industry, which not only reduces maintenance costs but also ensures safe equipment operation. At present, the monitoring method based on signal processing is one of the most common and effective fault diagnosis methods. In this work, the time-frequency distribution (TFD) obtained by generalized horizontal synchrosqueezing transform is used to extract the impulse feature of the non-stationary vibration signal of the tool. By using the TFD result, the two-dimensional (2D) Fourier transform can further detect the periodic pulses. Next, the energy proportion factor of periodic frequency point is proposed to evaluate the different tool wear degrees. Numerical simulations and experimental data analysis demonstrate the effectiveness of the proposed method as well as the potential for condition monitoring.","PeriodicalId":51184,"journal":{"name":"Structural Health Monitoring-An International Journal","volume":null,"pages":null},"PeriodicalIF":5.7000,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Structural Health Monitoring-An International Journal","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1177/14759217231192003","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0

Abstract

Mechanical system condition monitoring is an important procedure in modern industry, which not only reduces maintenance costs but also ensures safe equipment operation. At present, the monitoring method based on signal processing is one of the most common and effective fault diagnosis methods. In this work, the time-frequency distribution (TFD) obtained by generalized horizontal synchrosqueezing transform is used to extract the impulse feature of the non-stationary vibration signal of the tool. By using the TFD result, the two-dimensional (2D) Fourier transform can further detect the periodic pulses. Next, the energy proportion factor of periodic frequency point is proposed to evaluate the different tool wear degrees. Numerical simulations and experimental data analysis demonstrate the effectiveness of the proposed method as well as the potential for condition monitoring.
基于时频分析的脉冲特征提取方法在铣刀磨损定量评价中的应用
机械系统状态监测是现代工业中的一个重要环节,它不仅降低了维护成本,而且确保了设备的安全运行。目前,基于信号处理的监测方法是最常见、最有效的故障诊断方法之一。本文利用广义水平同步压缩变换得到的时频分布(TFD)来提取刀具非平稳振动信号的脉冲特征。通过使用TFD结果,二维(2D)傅立叶变换可以进一步检测周期性脉冲。其次,提出了周期频率点的能量比例因子来评价不同刀具磨损程度。数值模拟和实验数据分析证明了所提出方法的有效性以及状态监测的潜力。
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
CiteScore
12.80
自引率
12.10%
发文量
181
审稿时长
4.8 months
期刊介绍: Structural Health Monitoring is an international peer reviewed journal that publishes the highest quality original research that contain theoretical, analytical, and experimental investigations that advance the body of knowledge and its application in the discipline of structural health monitoring.
×
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