基于CEEMDAN-VSSLMS的滚动轴承故障诊断
TP391.9%TH133.33; 针对传统机械轴承故障诊断模型易受系统噪声干扰、特征识别效率低等问题,提出一种基于信号固有模式深度建模分析的轴承故障诊断方法.首先,将采集到的轴承振动信号进行噪声自适应完全经验模态分解(CEEMDAN),获得不同时间尺度的局部特征信号,使用相关系数判别并去除虚假模态分量,再利用可变步长最小均方算法(VSSLMS)对剩余IMF分量降噪并进行重构;然后,将降噪后的振动信号进行离散小波变换(DWT)得到时频谱图,并利用形态学开运算进行特征增强;最后利用改进GoogLeNet网络模型对特征图进行训练,通过Soft-max分类器完成特征归类,从而实现轴承故障诊断.将提出...
Saved in:
| Published in | 计算机集成制造系统 Vol. 30; no. 3; pp. 1138 - 1148 |
|---|---|
| Main Authors | , |
| Format | Journal Article |
| Language | Chinese |
| Published |
西安建筑科技大学信息与控制工程学院,陕西 西安 710055
31.03.2024
|
| Subjects | |
| Online Access | Get full text |
| ISSN | 1006-5911 |
| DOI | 10.13196/j.cims.2023.IM09 |
Cover
| Abstract | TP391.9%TH133.33; 针对传统机械轴承故障诊断模型易受系统噪声干扰、特征识别效率低等问题,提出一种基于信号固有模式深度建模分析的轴承故障诊断方法.首先,将采集到的轴承振动信号进行噪声自适应完全经验模态分解(CEEMDAN),获得不同时间尺度的局部特征信号,使用相关系数判别并去除虚假模态分量,再利用可变步长最小均方算法(VSSLMS)对剩余IMF分量降噪并进行重构;然后,将降噪后的振动信号进行离散小波变换(DWT)得到时频谱图,并利用形态学开运算进行特征增强;最后利用改进GoogLeNet网络模型对特征图进行训练,通过Soft-max分类器完成特征归类,从而实现轴承故障诊断.将提出的故障诊断方法应用于不同工况下的轴承故障数据集,试验结果表明,所提方法在噪声干扰下具有较高的诊断精度. |
|---|---|
| AbstractList | TP391.9%TH133.33; 针对传统机械轴承故障诊断模型易受系统噪声干扰、特征识别效率低等问题,提出一种基于信号固有模式深度建模分析的轴承故障诊断方法.首先,将采集到的轴承振动信号进行噪声自适应完全经验模态分解(CEEMDAN),获得不同时间尺度的局部特征信号,使用相关系数判别并去除虚假模态分量,再利用可变步长最小均方算法(VSSLMS)对剩余IMF分量降噪并进行重构;然后,将降噪后的振动信号进行离散小波变换(DWT)得到时频谱图,并利用形态学开运算进行特征增强;最后利用改进GoogLeNet网络模型对特征图进行训练,通过Soft-max分类器完成特征归类,从而实现轴承故障诊断.将提出的故障诊断方法应用于不同工况下的轴承故障数据集,试验结果表明,所提方法在噪声干扰下具有较高的诊断精度. |
| Abstract_FL | Aiming at the problems that the traditional mechanical bearing fault diagnosis model is easy to be disturbed by system noise and low efficiency of feature recognition,a bearing fault diagnosis method based on deep modeling a-nalysis of signal inherent mode was proposed.The collected bearing vibration signals were subjected to Complete Ensemble Empirical Mode Decomposition with Adaptive Noise(CEEMDAN)to obtain local characteristic signals of different time scales.Correlation coefficients were used to identify and remove false intrinsic mode function.The re-maining IMF components were denoised and reconstructed by Variable Step-Size Least Mean Square algorithm(VSSLMS).Then,the vibration signal after noise reduction was obtained by Discrete Wavelet Transform(DWT),and the feature was enhanced by morphological operation.The improved GoogLeNet network model was used to train the feature map,and the feature classification was completed by Softmax classifier,so as to realize the bearing fault diagnosis.The proposed fault diagnosis method was applied to the bearing fault data set under different work-ing conditions,and the test results showed that the diagnosis accuracy was higher under noise interference. |
| Author | 向世召 江莉 |
| AuthorAffiliation | 西安建筑科技大学信息与控制工程学院,陕西 西安 710055 |
| AuthorAffiliation_xml | – name: 西安建筑科技大学信息与控制工程学院,陕西 西安 710055 |
| Author_FL | JIANG Li XIANG Shizhao |
| Author_FL_xml | – sequence: 1 fullname: JIANG Li – sequence: 2 fullname: XIANG Shizhao |
| Author_xml | – sequence: 1 fullname: 江莉 – sequence: 2 fullname: 向世召 |
| BookMark | eNotjzFLw0AYhm-oYK39Af4FIen33eW79MYSoxYaHaKu5ZJepEFT8BSlsw52cNMhCK4u4uJUEf-MjfVfGNDp5Vmeh3eNNYpJYRjbQHBRoJKd3E3Hp9blwIXbj0A1WBMBpEMKcZW1rR0nNZIUPlGTdRZP86_5XRCG0VZvzzmK40EUf5fX1Xu5mD0vP96q28_q_uanfFy-zqqHl3W2kukTa9r_22KH2-FBsOsM9nf6QW_gWARSjlFK-yAyyEacexwTkaYCyAc14jIVUqHfTbxMGJkkRH5XGvJQE_GENDeIosU2_7yXush0cTzMJxdnRV0c5jbP0-n06rx-6IEArsQvOZxS0w |
| ClassificationCodes | TP391.9%TH133.33 |
| 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 | 10.13196/j.cims.2023.IM09 |
| 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 |
| DocumentTitle_FL | Rolling bearing fault diagnosis based on CEEMDAN-VSSLMS |
| EndPage | 1148 |
| ExternalDocumentID | jsjjczzxt202403029 |
| GroupedDBID | 2B. 4A8 92I 93N ALMA_UNASSIGNED_HOLDINGS CDYEO PSX TCJ |
| ID | FETCH-LOGICAL-s1059-e99a703f0fd22421b3cc305709d26c369178b4f3e6bb55786e541a552b5a2e113 |
| ISSN | 1006-5911 |
| IngestDate | Thu May 29 04:00:06 EDT 2025 |
| IsPeerReviewed | true |
| IsScholarly | true |
| Issue | 3 |
| Keywords | GoogLeNet model empirical mode decomposition 离散小波变换 最小均方算法 经验模态分解 discrete wavelet transform 轴承故障诊断 GoogLeNet模型 bearing fault diagnosis least mean square algorithm |
| Language | Chinese |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-s1059-e99a703f0fd22421b3cc305709d26c369178b4f3e6bb55786e541a552b5a2e113 |
| PageCount | 11 |
| ParticipantIDs | wanfang_journals_jsjjczzxt202403029 |
| PublicationCentury | 2000 |
| PublicationDate | 2024-03-31 |
| PublicationDateYYYYMMDD | 2024-03-31 |
| PublicationDate_xml | – month: 03 year: 2024 text: 2024-03-31 day: 31 |
| PublicationDecade | 2020 |
| PublicationTitle | 计算机集成制造系统 |
| PublicationTitle_FL | Computer Integrated Manufacturing Systems |
| PublicationYear | 2024 |
| Publisher | 西安建筑科技大学信息与控制工程学院,陕西 西安 710055 |
| Publisher_xml | – name: 西安建筑科技大学信息与控制工程学院,陕西 西安 710055 |
| SSID | ssib006563755 ssib023646381 ssib001102950 ssib051375755 ssib023167363 ssib036438063 ssib000459500 ssib002258428 |
| Score | 2.410436 |
| Snippet | TP391.9%TH133.33;... |
| SourceID | wanfang |
| SourceType | Aggregation Database |
| StartPage | 1138 |
| Title | 基于CEEMDAN-VSSLMS的滚动轴承故障诊断 |
| URI | https://d.wanfangdata.com.cn/periodical/jsjjczzxt202403029 |
| Volume | 30 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| link | http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnR09bxMx1OrHwoJAgPhWJfBUXXvns8_2eJf4VFDTJS2qxFDdV2gjESSSSigzDHRggyFCYmVBLExFiD9DQ_kXvOe4yUE7FJbTi9-79-F35_d8sZ8JuR9kpc8qwT2MBx5XPPQypjteWeIEo9BlYItVtzaitS3-cFtsz80_rq1a2h_kK8XwzH0l_-NVaAO_4i7Zf_DslCk0AAz-hSt4GK7n8jE1guqUJjE1HK_KNIxpNeMN71G7vd5qUyOphmZOTUSTBGG4Q8U0VtQomjRpYlFK0yRFQANWUKORUjeQJk6RHlERdaUbuyfCAWtoHKAUALS0ZA2rDnBIqIosc0W1b-WCxAhRyqexj3clIWqFAOg2XWJslQ3QMJCgDFV6hhHISwfWXoU6Id-UugKS7usF4yfb-dzzZlUV1kaBqoK9AIBQVBWUb1qWAEgLRGiysjrHHBsRaNI4smJTazLQBNg5E0VAS2wxjthZCiIkykXOQJPU-ED_aBozeMAdDD1_ouTyaXWXsTrSpNCwiyD4iUZoF0FciHF_Pe3Vv0DYeBEEk9o2LvfAyemZcQ0HShvYir2nWGSehSsPWr6eBfHp0spuv9sthsMXA-xtGMGZnieLTEpcw7AYN1vr7Xo2r0WtOiNkgkyL-rZoyE5rs1NI_UM528bMsIhCrXobHkUAo_k0XMDPUPkzvAjgbmnPIZ52kltngOat_m2c3UbX62S9J7WMb_MSueimakvx5L27TOaGu1fI6tGHwx-Hb_58z36OXo6_jo4OPh5_-zJ-_X389tWv0fvjzwfjd5-ukq3UbDbWPHfoiNfHqYZXaZ1BFOz4nZLhcok8LAqIidLXJYuKMNKBVDnvhFWU5wLCXQQDXZAJwXKRsQr8eY0s9J71qutkSQZVIWVZcdnJORayDHWZZZrhmXCcq-wGuecM3HGDSn_ntPtunovqFrkwe71uk4XB8_3qDqTLg_yuc_tvG_-D0A |
| linkProvider | EBSCOhost |
| 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%8ECEEMDAN-VSSLMS%E7%9A%84%E6%BB%9A%E5%8A%A8%E8%BD%B4%E6%89%BF%E6%95%85%E9%9A%9C%E8%AF%8A%E6%96%AD&rft.jtitle=%E8%AE%A1%E7%AE%97%E6%9C%BA%E9%9B%86%E6%88%90%E5%88%B6%E9%80%A0%E7%B3%BB%E7%BB%9F&rft.au=%E6%B1%9F%E8%8E%89&rft.au=%E5%90%91%E4%B8%96%E5%8F%AC&rft.date=2024-03-31&rft.pub=%E8%A5%BF%E5%AE%89%E5%BB%BA%E7%AD%91%E7%A7%91%E6%8A%80%E5%A4%A7%E5%AD%A6%E4%BF%A1%E6%81%AF%E4%B8%8E%E6%8E%A7%E5%88%B6%E5%B7%A5%E7%A8%8B%E5%AD%A6%E9%99%A2%2C%E9%99%95%E8%A5%BF+%E8%A5%BF%E5%AE%89+710055&rft.issn=1006-5911&rft.volume=30&rft.issue=3&rft.spage=1138&rft.epage=1148&rft_id=info:doi/10.13196%2Fj.cims.2023.IM09&rft.externalDocID=jsjjczzxt202403029 |
| thumbnail_s | http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fwww.wanfangdata.com.cn%2Fimages%2FPeriodicalImages%2Fjsjjczzxt%2Fjsjjczzxt.jpg |