基于EMD分量与小波包能量熵的轧辊磨削颤振在线预测
TG58; 针对轧辊磨削颤振时的时频域单一处理方法存在部分特征丢失的问题,提出了时频域相结合的方法对信号进行特征处理,并利用智能算法实现轧辊磨削颤振的在线预测.首先,利用经验模态分解(empirical mode decomposition,EMD)方法对振动传感器信号进行分解获得各固有模态函数(intrinsic mode function,IMF),剔除"虚假分量"后计算表征轧辊磨削颤振的时域特征.然后,利用小波包能量熵对声发射传感器信号求解频率段节点能量熵值,获得表征轧辊磨削颤振的频域特征.最后,将上述时频域特征降维后代入智能算法模型实现对轧辊磨削加工的在线预测.结果...
Saved in:
| Published in | 金刚石与磨料磨具工程 Vol. 44; no. 1; pp. 73 - 84 |
|---|---|
| Main Authors | , , , , |
| Format | Journal Article |
| Language | Chinese |
| Published |
上海工程技术大学 高职学院, 上海 200437%上海理工大学 机械工程学院, 上海 200093
01.02.2024
上海市高级技工学校 制造工程系, 上海 200437 |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1006-852X |
| DOI | 10.13394/j.cnki.jgszz.2022.0198 |
Cover
| Abstract | TG58; 针对轧辊磨削颤振时的时频域单一处理方法存在部分特征丢失的问题,提出了时频域相结合的方法对信号进行特征处理,并利用智能算法实现轧辊磨削颤振的在线预测.首先,利用经验模态分解(empirical mode decomposition,EMD)方法对振动传感器信号进行分解获得各固有模态函数(intrinsic mode function,IMF),剔除"虚假分量"后计算表征轧辊磨削颤振的时域特征.然后,利用小波包能量熵对声发射传感器信号求解频率段节点能量熵值,获得表征轧辊磨削颤振的频域特征.最后,将上述时频域特征降维后代入智能算法模型实现对轧辊磨削加工的在线预测.结果表明:LV-SVM模型的磨削颤振分类平均准确率达92.75%,模型平均响应时间为 0.776 5 s;验证了时频域特性的EMD和小波包能量熵方法的LV-SVM在线预测轧辊磨削颤振的有效性. |
|---|---|
| AbstractList | TG58; 针对轧辊磨削颤振时的时频域单一处理方法存在部分特征丢失的问题,提出了时频域相结合的方法对信号进行特征处理,并利用智能算法实现轧辊磨削颤振的在线预测.首先,利用经验模态分解(empirical mode decomposition,EMD)方法对振动传感器信号进行分解获得各固有模态函数(intrinsic mode function,IMF),剔除"虚假分量"后计算表征轧辊磨削颤振的时域特征.然后,利用小波包能量熵对声发射传感器信号求解频率段节点能量熵值,获得表征轧辊磨削颤振的频域特征.最后,将上述时频域特征降维后代入智能算法模型实现对轧辊磨削加工的在线预测.结果表明:LV-SVM模型的磨削颤振分类平均准确率达92.75%,模型平均响应时间为 0.776 5 s;验证了时频域特性的EMD和小波包能量熵方法的LV-SVM在线预测轧辊磨削颤振的有效性. |
| Abstract_FL | To address the issue of partial feature loss in the single processing method within the time-frequency do-main for roll grinding chatter,a combined time-frequency domain method is proposed to process signal feature.An in-telligent algorithm is used to achieve online prediction of roll grinding chatter.Firstly,the empirical mode decomposi-tion(EMD)method is utilized to decompose the vibration sensor signals,extrating the intrinsic mode function(IMF)while removing"spurious components"to calculate time domain characteristics associated with roll grinding chatter.Then,wavelet packet energy entropy is used to solve the frequency band node energy entropy values of acoustic emis-sion sensor signals,obtaining frequency domain features characterizing the roll grinding chatter.Finally,the time-fre-quency domain features after dimension reduction is substituted into the intelligent algorithm model for online predic-tion of the roller grinding process.The results show that the the LV-SVM model achieves an average classification ac-curacy of 92.75%,with an average response time of 0.776 5 s.This verifies the validity of EMD and LV-SVM based on wavelet packet energy entropy in the time-frequency domain for online prediction of roller grinding chatter. |
| Author | 朱欢欢 张梦梦 应晓昂 迟玉伦 熊力 |
| AuthorAffiliation | 上海市高级技工学校 制造工程系, 上海 200437;上海工程技术大学 高职学院, 上海 200437%上海理工大学 机械工程学院, 上海 200093 |
| AuthorAffiliation_xml | – name: 上海市高级技工学校 制造工程系, 上海 200437;上海工程技术大学 高职学院, 上海 200437%上海理工大学 机械工程学院, 上海 200093 |
| Author_FL | CHI Yulun YING Xiaoang ZHU Huanhuan ZHANG Mengmeng XIONG Li |
| Author_FL_xml | – sequence: 1 fullname: ZHU Huanhuan – sequence: 2 fullname: CHI Yulun – sequence: 3 fullname: ZHANG Mengmeng – sequence: 4 fullname: XIONG Li – sequence: 5 fullname: YING Xiaoang |
| Author_xml | – sequence: 1 fullname: 朱欢欢 – sequence: 2 fullname: 迟玉伦 – sequence: 3 fullname: 张梦梦 – sequence: 4 fullname: 熊力 – sequence: 5 fullname: 应晓昂 |
| BookMark | eNotj81KAlEYhs_CIDOvoQuImb7vnHHOnGWY_YDRpqCdzM854qQjNETkNrMy0X0EwRTkriCEEKybcRznLhoo3sUL7-J5edZILmgHkpANBB0ZE8aWr7vBWUP362Gno1OgVAcUVo7kEcDUrBI9XSXFMGw4UELGTaQ8Tyrx83Q-HVYOd-K7Xno7mn8N44_R4jOKBzfL61m2JL1J8thdzt6W3_3kZRzf99PodTF4j5_GyfQnjbqLycM6WVF2M5TF_y6Qk93KcXlfqx7tHZS3q1qIYILGDekpZI4BhmV5jmCcK-VJzkwHmTBtDyyXSXQdV4AtPEtIJaiLDqBiaFPJCmTzj3tpB8oO6jW_fXEeZI-1TPqq1Wz5dTcTNwCzsF91CWrR |
| ClassificationCodes | TG58 |
| ContentType | Journal Article |
| Copyright | Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
| Copyright_xml | – notice: Copyright © Wanfang Data Co. Ltd. All Rights Reserved. |
| DBID | 2B. 4A8 92I 93N PSX TCJ |
| DOI | 10.13394/j.cnki.jgszz.2022.0198 |
| DatabaseName | Wanfang Data Journals - Hong Kong WANFANG Data Centre Wanfang Data Journals 万方数据期刊 - 香港版 China Online Journals (COJ) China Online Journals (COJ) |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Engineering |
| DocumentTitle_FL | On line prediction of roll grinding chatter based on EMD component and wavelet packet energy entropy |
| EndPage | 84 |
| ExternalDocumentID | jgsymlmjgc202401010 |
| GroupedDBID | -03 2B. 4A8 5XA 5XC 92H 92I 93N ABJNI ACGFS ALMA_UNASSIGNED_HOLDINGS ARCSS CCEZO CEKLB CW9 GROUPED_DOAJ PSX TCJ TGT U1G U5L |
| ID | FETCH-LOGICAL-s1060-74edf13b40488db9377ffde736b1396ad08c3e1cbc90a9d89ef92c1b01f31a2e3 |
| ISSN | 1006-852X |
| IngestDate | Thu May 29 03:59:00 EDT 2025 |
| IsPeerReviewed | false |
| IsScholarly | true |
| Issue | 1 |
| Keywords | least squares support vector machine(LS-SVM) 小波包能量熵 roll grinding chatter EMD分解 固有模态函数 最小二乘支持向量机 EMD decomposition wavelet energy entropy 轧辊磨削颤振 intrinsic mode function(IMF) |
| Language | Chinese |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-s1060-74edf13b40488db9377ffde736b1396ad08c3e1cbc90a9d89ef92c1b01f31a2e3 |
| PageCount | 12 |
| ParticipantIDs | wanfang_journals_jgsymlmjgc202401010 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-02-01 |
| PublicationDateYYYYMMDD | 2024-02-01 |
| PublicationDate_xml | – month: 02 year: 2024 text: 2024-02-01 day: 01 |
| PublicationDecade | 2020 |
| PublicationTitle | 金刚石与磨料磨具工程 |
| PublicationTitle_FL | Diamond & Abrasives Engineering |
| PublicationYear | 2024 |
| Publisher | 上海工程技术大学 高职学院, 上海 200437%上海理工大学 机械工程学院, 上海 200093 上海市高级技工学校 制造工程系, 上海 200437 |
| Publisher_xml | – name: 上海工程技术大学 高职学院, 上海 200437%上海理工大学 机械工程学院, 上海 200093 – name: 上海市高级技工学校 制造工程系, 上海 200437 |
| SSID | ssib051376127 ssib002263492 ssj0039779 ssib023167358 ssib008143579 ssib001129951 |
| Score | 2.3905354 |
| Snippet | TG58; 针对轧辊磨削颤振时的时频域单一处理方法存在部分特征丢失的问题,提出了时频域相结合的方法对信号进行特征处理,并利用智能算法实现轧辊磨削颤振的在线预测.首先,利... |
| SourceID | wanfang |
| SourceType | Aggregation Database |
| StartPage | 73 |
| Title | 基于EMD分量与小波包能量熵的轧辊磨削颤振在线预测 |
| URI | https://d.wanfangdata.com.cn/periodical/jgsymlmjgc202401010 |
| Volume | 44 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVAON databaseName: DOAJ (Directory of Open Access Journals) issn: 1006-852X databaseCode: DOA dateStart: 20220101 customDbUrl: isFulltext: true dateEnd: 99991231 titleUrlDefault: https://www.doaj.org/ omitProxy: true ssIdentifier: ssj0039779 providerName: Directory of Open Access Journals |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnR1Na9RANNR60YP4id_00DlJ1kxmkswck22WItRTC72VJJtUa7uCbQ_u1Vq1lvYuglAFe1OQghSqf6bbbf-F771ksynmUIVlmH3z5s37yMybN5mZGMZ4zCN4cNzUzLiVmFJn0ozjlJupbifQA-MkEngaeeqxOzkjH806syOj05VdS6srcSPp1p4r-R-rAgzsiqdk_8GyJVEAQB7sCylYGNIz2ZiFDtMtFvgslJiqMJyaQKBSTLks1AxCfdWiUoCEWBRYBHFZIJhvE3KTKYeFgCBYMFGp5SGRwMGMBuIScQDB9ygTMuVjkQ90FNHRBNFI1pfYBFD2W8Rkk3A8ZDJoFTiKcIC-CqpT5IIBzQtBNLWCYoqKIGW7LtPw06c5ATE9EhaAxD_AVflyiSo1WcAx4zdJDUVmiKKQUZ1rIUTZsG1AcYcoDgEsqgwiu2VmiEIqRK04mOqgushiy8G-7KJbDMTzC73UyUBq9XF_Si5Erl9Qt0_IPpjHfUAKDphWZFWOdhiUYZEm9UPf-rtBumES70dw6pgBM1j0YJVc1TSd69YvlAGPW40MZ2fGyr9sWfhNXJhSjj1bdaz5xZ6nBpDcS-YfrxnMt2StJxdCS3LlSefZ08bC_HK32wDL2A2ISdRw8lJuKQWMl0uLSwvzCdoPb060zhnnbfD1VmWhhYIEmOLqyrtwCEDwis7yv8IQYvju2sa7IsRw74DDwSdzu1zWwQhK056KQgXFLlIU4GE9-3RAsJNFnfnKXHb6snGpCELH_HxEuWKMdJ9cNS5Wria9ZoS9T_uH-1swnvTerp-82T78udX7vn30Y6e3-fr41QFA-ut7_Q9rxwdfj39t9D_v9t5tnOx8Odr81vu429__fbKzdrT3_rox0wqnm5Nm8ckVc5lbrmV6Mm1nXMQSHXs7htjFy7J2CqN5DKGiG7UtlYiUJ3GirUi3lU4zbSc8tngmeGSn4oYx2nneSW8aY16mBYQ3tsejSKaxjLUSUkVCpNLJMhHfMsYLJcwVQ-ryXI0Rb58N7Y5xYdhv7xqjKy9W03sQLqzE98n6fwCgvM3d |
| linkProvider | Directory of Open Access Journals |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=%E5%9F%BA%E4%BA%8EEMD%E5%88%86%E9%87%8F%E4%B8%8E%E5%B0%8F%E6%B3%A2%E5%8C%85%E8%83%BD%E9%87%8F%E7%86%B5%E7%9A%84%E8%BD%A7%E8%BE%8A%E7%A3%A8%E5%89%8A%E9%A2%A4%E6%8C%AF%E5%9C%A8%E7%BA%BF%E9%A2%84%E6%B5%8B&rft.jtitle=%E9%87%91%E5%88%9A%E7%9F%B3%E4%B8%8E%E7%A3%A8%E6%96%99%E7%A3%A8%E5%85%B7%E5%B7%A5%E7%A8%8B&rft.au=%E6%9C%B1%E6%AC%A2%E6%AC%A2&rft.au=%E8%BF%9F%E7%8E%89%E4%BC%A6&rft.au=%E5%BC%A0%E6%A2%A6%E6%A2%A6&rft.au=%E7%86%8A%E5%8A%9B&rft.date=2024-02-01&rft.pub=%E4%B8%8A%E6%B5%B7%E5%B7%A5%E7%A8%8B%E6%8A%80%E6%9C%AF%E5%A4%A7%E5%AD%A6+%E9%AB%98%E8%81%8C%E5%AD%A6%E9%99%A2%2C+%E4%B8%8A%E6%B5%B7+200437%25%E4%B8%8A%E6%B5%B7%E7%90%86%E5%B7%A5%E5%A4%A7%E5%AD%A6+%E6%9C%BA%E6%A2%B0%E5%B7%A5%E7%A8%8B%E5%AD%A6%E9%99%A2%2C+%E4%B8%8A%E6%B5%B7+200093&rft.issn=1006-852X&rft.volume=44&rft.issue=1&rft.spage=73&rft.epage=84&rft_id=info:doi/10.13394%2Fj.cnki.jgszz.2022.0198&rft.externalDocID=jgsymlmjgc202401010 |
| thumbnail_s | http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fwww.wanfangdata.com.cn%2Fimages%2FPeriodicalImages%2Fjgsymlmjgc%2Fjgsymlmjgc.jpg |