基于Nadam-TimeGAN和XGBoost的时序信号故障诊断方法

TP18; 为了提高故障诊断模型在数据不平衡场景下的诊断性能和模型泛化能力,提出了一种基于Nadam-TimeGAN和XGBoost的时序信号故障诊断方法.首先对比基于LSTM和GRU的TimeGAN模型,选取性能更优的GRU网络作为TimeGAN模型的组成单元,然后采用Nadam优化算法对TimeGAN模型的各组件进行优化,即构建Nadam-TimeGAN模型用以数据扩充,最后构建一个平衡的数据集输入XGBoost集成学习模型进行分类训练.实验选取转辙机动作电流数据集进行验证性实验,选取MFPT轴承数据集和CWRU轴承数据集进行泛化性实验,并与8种方法进行对比,结果表明,所提方法在准确率、召...

Full description

Saved in:
Bibliographic Details
Published in通信学报 Vol. 45; no. 4; pp. 185 - 200
Main Authors 黑新宏, 高苗, 张宽, 费蓉, 邱原, 姬文江
Format Journal Article
LanguageChinese
Published 陕西省网络计算与安全技术重点实验室,陕西 西安 710048%西安理工大学计算机科学与工程学院,陕西 西安 710048 30.04.2024
西安理工大学计算机科学与工程学院,陕西 西安 710048
Subjects
TimeGAN
fault diagnosis
Nesterov加速自适应矩估计
极致梯度提升
data augmentation
故障诊断
Nadam
数据增强
XGBoost
时间序列生成对抗网络
Online AccessGet full text
ISSN1000-436X
DOI10.11959/j.issn.1000-436x.2024081

Cover

Abstract TP18; 为了提高故障诊断模型在数据不平衡场景下的诊断性能和模型泛化能力,提出了一种基于Nadam-TimeGAN和XGBoost的时序信号故障诊断方法.首先对比基于LSTM和GRU的TimeGAN模型,选取性能更优的GRU网络作为TimeGAN模型的组成单元,然后采用Nadam优化算法对TimeGAN模型的各组件进行优化,即构建Nadam-TimeGAN模型用以数据扩充,最后构建一个平衡的数据集输入XGBoost集成学习模型进行分类训练.实验选取转辙机动作电流数据集进行验证性实验,选取MFPT轴承数据集和CWRU轴承数据集进行泛化性实验,并与8种方法进行对比,结果表明,所提方法在准确率、召回率以及F1-score这3种评价指标上均高于其他方法,从而验证了所提方法在不平衡数据故障诊断方面的有效性和泛化性.
AbstractList TP18; 为了提高故障诊断模型在数据不平衡场景下的诊断性能和模型泛化能力,提出了一种基于Nadam-TimeGAN和XGBoost的时序信号故障诊断方法.首先对比基于LSTM和GRU的TimeGAN模型,选取性能更优的GRU网络作为TimeGAN模型的组成单元,然后采用Nadam优化算法对TimeGAN模型的各组件进行优化,即构建Nadam-TimeGAN模型用以数据扩充,最后构建一个平衡的数据集输入XGBoost集成学习模型进行分类训练.实验选取转辙机动作电流数据集进行验证性实验,选取MFPT轴承数据集和CWRU轴承数据集进行泛化性实验,并与8种方法进行对比,结果表明,所提方法在准确率、召回率以及F1-score这3种评价指标上均高于其他方法,从而验证了所提方法在不平衡数据故障诊断方面的有效性和泛化性.
Abstract_FL In order to improve the diagnostic performance and model generalization ability of the fault diagnosis model in data imbalance scenarios,a time series signal fault diagnosis method based on Nadam-TimeGAN and XGBoost was proposed.Firstly,the TimeGAN model based on LSTM and GRU was compared,and the GRU network with better per-formance was selected as the component unit of the TimeGAN model.The Nadam optimization algorithm was used to optimize the components of the TimeGAN model,that was,the Nadam-TimeGAN model was constructed for data expan-sion.After data expansion,a balanced data set was constructed and input into the XGBoost integrated learning model for classification training.In the experiment,the action current data set of switch machine was selected for verification ex-periment,the MFPT bearing data set and the CWRU bearing data set were selected for generalization experiment,and compared with eight methods.The results show that the proposed method is higher than other methods in accuracy,recall and F1-score.The experimental results validate the effectiveness and generalization of the proposed method for imbal-anced data fault diagnosis.
Author 黑新宏
费蓉
姬文江
高苗
张宽
邱原
AuthorAffiliation 西安理工大学计算机科学与工程学院,陕西 西安 710048;陕西省网络计算与安全技术重点实验室,陕西 西安 710048%西安理工大学计算机科学与工程学院,陕西 西安 710048
AuthorAffiliation_xml – name: 西安理工大学计算机科学与工程学院,陕西 西安 710048;陕西省网络计算与安全技术重点实验室,陕西 西安 710048%西安理工大学计算机科学与工程学院,陕西 西安 710048
Author_FL ZHANG Kuan
QIU Yuan
GAO Miao
HEI Xinhong
JI Wenjiang
FEI Rong
Author_FL_xml – sequence: 1
  fullname: HEI Xinhong
– sequence: 2
  fullname: GAO Miao
– sequence: 3
  fullname: ZHANG Kuan
– sequence: 4
  fullname: FEI Rong
– sequence: 5
  fullname: QIU Yuan
– sequence: 6
  fullname: JI Wenjiang
Author_xml – sequence: 1
  fullname: 黑新宏
– sequence: 2
  fullname: 高苗
– sequence: 3
  fullname: 张宽
– sequence: 4
  fullname: 费蓉
– sequence: 5
  fullname: 邱原
– sequence: 6
  fullname: 姬文江
BookMark eNrjYmDJy89LZWBQNDTQMzS0NLXUz9LLLC7O0zM0MDDQNTE2q9AzMjAyMbAwZGHghIlFcDDwFhdnJhmYGhqbmxkYG3Iy2D2dv-vJrj6_xJTEXN2QzNxUd0e_p5N6Ityd8vOLS57Pank2fdvTXf1P9i982r_92dTWl7PmvFjf9Wza2mfTdj7bPJWHgTUtMac4lRdKczOEurmGOHvo-vi7ezo7-ugWA-02001LTE4xTjEyT0xMNkoytzQ1SzZJsjAxNE0yM7EwNk01NjSzSDWyMDNJNktMMbEwNTQwtEw1MjU2M0pNTrZMMzQ05mZQhZhbnpiXlpiXHp-VX1qUB7QxvqSiIgnsUxMDQzNjALHqXFM
ClassificationCodes TP18
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 10.11959/j.issn.1000-436x.2024081
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 Fault diagnosis method of timing signal based on Nadam-TimeGAN and XGBoost
EndPage 200
ExternalDocumentID txxb202404016
GroupedDBID -0Y
2B.
4A8
92I
93N
ALMA_UNASSIGNED_HOLDINGS
CCEZO
CUBFJ
GROUPED_DOAJ
PSX
TCJ
ID FETCH-LOGICAL-s1006-facd3d27aac2b7956c4b8415b64835e3168e2864c6ad4851019e25362ecc9f113
ISSN 1000-436X
IngestDate Thu May 29 04:00:49 EDT 2025
IsPeerReviewed false
IsScholarly true
Issue 4
Keywords TimeGAN
fault diagnosis
Nesterov加速自适应矩估计
极致梯度提升
data augmentation
故障诊断
Nadam
数据增强
XGBoost
时间序列生成对抗网络
Language Chinese
LinkModel OpenURL
MergedId FETCHMERGED-LOGICAL-s1006-facd3d27aac2b7956c4b8415b64835e3168e2864c6ad4851019e25362ecc9f113
PageCount 16
ParticipantIDs wanfang_journals_txxb202404016
PublicationCentury 2000
PublicationDate 2024-04-30
PublicationDateYYYYMMDD 2024-04-30
PublicationDate_xml – month: 04
  year: 2024
  text: 2024-04-30
  day: 30
PublicationDecade 2020
PublicationTitle 通信学报
PublicationTitle_FL Journal on Communications
PublicationYear 2024
Publisher 陕西省网络计算与安全技术重点实验室,陕西 西安 710048%西安理工大学计算机科学与工程学院,陕西 西安 710048
西安理工大学计算机科学与工程学院,陕西 西安 710048
Publisher_xml – name: 西安理工大学计算机科学与工程学院,陕西 西安 710048
– name: 陕西省网络计算与安全技术重点实验室,陕西 西安 710048%西安理工大学计算机科学与工程学院,陕西 西安 710048
SSID ssib051376031
ssj0002912165
ssib058759023
ssib001102965
ssib023646527
ssib023168036
ssib036439991
ssib050281523
ssib000968473
Score 2.4333932
Snippet TP18; 为了提高故障诊断模型在数据不平衡场景下的诊断性能和模型泛化能力,提出了一种基于Nadam-TimeGAN和XGBoost的时序信号故障诊断方法.首先对比基于LSTM和GRU的TimeGAN...
SourceID wanfang
SourceType Aggregation Database
StartPage 185
Title 基于Nadam-TimeGAN和XGBoost的时序信号故障诊断方法
URI https://d.wanfangdata.com.cn/periodical/txxb202404016
Volume 45
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVAON
  databaseName: DOAJ Directory of Open Access Journals
  issn: 1000-436X
  databaseCode: DOA
  dateStart: 20230101
  customDbUrl:
  isFulltext: true
  dateEnd: 99991231
  titleUrlDefault: https://www.doaj.org/
  omitProxy: true
  ssIdentifier: ssj0002912165
  providerName: Directory of Open Access Journals
– providerCode: PRVHPJ
  databaseName: ROAD: Directory of Open Access Scholarly Resources
  issn: 1000-436X
  databaseCode: M~E
  dateStart: 19800101
  customDbUrl:
  isFulltext: true
  dateEnd: 99991231
  titleUrlDefault: https://road.issn.org
  omitProxy: true
  ssIdentifier: ssib058759023
  providerName: ISSN International Centre
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LaxRBEB5iBPEiiorPEME-hV1nevp5EXpmZxMEc0pgb2Feqxc3kGwg5KwXEQNCIgRB_AHiQVDMxT-TXfVfWNXzDIlovAy9XdU99VXNdlX3TFc7zv1cplnGMrfj5jLrMJ36Hc0zCOSSISawGsa5Tdf0eFksrbJHAz6Yme23vlraGifddOfUfSX_Y1WoA7viLtkzWLbuFCqgDPaFK1gYrv9kYxJxovskMCRieFXRcpzFzzq4r2PRLFsyJSocLAbruLkjkkQDFyORIFqSQCAHtuvbDvrEeFgDPwNpeaDMSaSxlQ5JpIjpE2UsSRDTKwuBxkLgk-IgyyrWxYb4HYU51jm0MgL5oR_DK3sjbxAQ7VVdupY3AlnaLAZYFMqhoCAbCqAIianaBL2GokjArICKaJ8U5whVqxyUtV7Y4HNpoWqEjVA5Co0qCyHcxkLQswJKK2mhjsiiklgAhSJOkC2qZNdWm1BWJWBQB-ILUY-oHUlUD5sramW0rXRkkQJzWNYYBn-Ek7IttKQs77eAWZSYIpSfpFkoLlGF0SVSkcSIkS27nMRk5cVHTCKnbhuxBbfsUCJWME7DY6U29IwIWr4SkxIw3x5EWTvTIjdoOWiwlmf0ipOZyiCrSE97iv_WXFsHjnfoVnfY7lKbis9rgpb6U9Lx9nZiqQwmL-ec81QKQVtLK-U0HYKw9v5xl-rm7T7Fw9sgkmt-CyY4rcNyH6N23eSU4hCTQ9hb98c9_LKseZvOlcSkSH691ku1Rz17qm2tswvOvQrvgz-htfsIR8N49KQV8q5cdi6Vc9V5Uww8V5yZnadXnYeT94dHh6-PDTSTN6_KIebHwfPp2y-Tw92j7x8mu1-ney9-Hbz7-enldP_jdP_b9PPeNWe1H62ES53yEJbOpoerjcM4zfyMyjhOaSI1FylLFET9iWAwectRdTlVgqUizphCD69zyiEsBt-gh57nX3dmR-uj_IYz7_pacfD3aZZnMG9TgF24CQQMrk7k0GM3nbkS71o5yG6uHTPvrb8x3HYuNoPHHWd2vLGV34VJwziZs0_Eb8u8wUc
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%8ENadam-TimeGAN%E5%92%8CXGBoost%E7%9A%84%E6%97%B6%E5%BA%8F%E4%BF%A1%E5%8F%B7%E6%95%85%E9%9A%9C%E8%AF%8A%E6%96%AD%E6%96%B9%E6%B3%95&rft.jtitle=%E9%80%9A%E4%BF%A1%E5%AD%A6%E6%8A%A5&rft.au=%E9%BB%91%E6%96%B0%E5%AE%8F&rft.au=%E9%AB%98%E8%8B%97&rft.au=%E5%BC%A0%E5%AE%BD&rft.au=%E8%B4%B9%E8%93%89&rft.date=2024-04-30&rft.pub=%E9%99%95%E8%A5%BF%E7%9C%81%E7%BD%91%E7%BB%9C%E8%AE%A1%E7%AE%97%E4%B8%8E%E5%AE%89%E5%85%A8%E6%8A%80%E6%9C%AF%E9%87%8D%E7%82%B9%E5%AE%9E%E9%AA%8C%E5%AE%A4%2C%E9%99%95%E8%A5%BF+%E8%A5%BF%E5%AE%89+710048%25%E8%A5%BF%E5%AE%89%E7%90%86%E5%B7%A5%E5%A4%A7%E5%AD%A6%E8%AE%A1%E7%AE%97%E6%9C%BA%E7%A7%91%E5%AD%A6%E4%B8%8E%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+710048&rft.issn=1000-436X&rft.volume=45&rft.issue=4&rft.spage=185&rft.epage=200&rft_id=info:doi/10.11959%2Fj.issn.1000-436x.2024081&rft.externalDocID=txxb202404016
thumbnail_s http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fwww.wanfangdata.com.cn%2Fimages%2FPeriodicalImages%2Ftxxb%2Ftxxb.jpg