基于LMKL和OC-ELM的航空电子部件故障检测方法

V243; 针对航空电子部件故障样本获取困难以及检测准确率不高的问题,提出基于局部多核学习(localized multiple kernel learning,LMKL)和一类超限学习机(one-class extreme learning machine,OC-ELM)的故障检测方法.仅运用正常状态的小样本数据,给出了LMK-OC-ELM的数学表达形式,并在不同的门模型下推导了LMK-OC-ELM中局部核权重的优化方法;在获取局部核权重的基础上,定义了离线故障检测所需的统计检验量与阈值,以便工程实现.将所提方法应用于某型接收机,结果表明,在训练时间可控的前提下,与4种常见的一类分类(one...

Full description

Saved in:
Bibliographic Details
Published in系统工程与电子技术 no. 6; pp. 1424 - 1432
Main Authors 朱敏, 刘奇, 刘星, 许晴
Format Journal Article
LanguageChinese
Published 海军航空大学,山东烟台,264001%海军装备部,北京,100841%中国人民解放军92228部队,北京,100010 01.06.2020
Subjects
局部多核学习
故障检测
一类分类
超限学习机
Online AccessGet full text

Cover

Abstract V243; 针对航空电子部件故障样本获取困难以及检测准确率不高的问题,提出基于局部多核学习(localized multiple kernel learning,LMKL)和一类超限学习机(one-class extreme learning machine,OC-ELM)的故障检测方法.仅运用正常状态的小样本数据,给出了LMK-OC-ELM的数学表达形式,并在不同的门模型下推导了LMK-OC-ELM中局部核权重的优化方法;在获取局部核权重的基础上,定义了离线故障检测所需的统计检验量与阈值,以便工程实现.将所提方法应用于某型接收机,结果表明,在训练时间可控的前提下,与4种常见的一类分类(one-class classification,OCC)算法相比,所提方法可均衡地提高召回率、查准率和特异度,以LMK-OC-ELM-sig为代表,其在F1、曲线下方面积(area under curve,AUC)、G-mean和准确率4个指标上,比最近提出的局部多核异常检测(localized multiple kernel anomaly detection,LMKAD)方法分别提高了1.60％、1.57％、1.53％和2.23％.
AbstractList V243; 针对航空电子部件故障样本获取困难以及检测准确率不高的问题,提出基于局部多核学习(localized multiple kernel learning,LMKL)和一类超限学习机(one-class extreme learning machine,OC-ELM)的故障检测方法.仅运用正常状态的小样本数据,给出了LMK-OC-ELM的数学表达形式,并在不同的门模型下推导了LMK-OC-ELM中局部核权重的优化方法;在获取局部核权重的基础上,定义了离线故障检测所需的统计检验量与阈值,以便工程实现.将所提方法应用于某型接收机,结果表明,在训练时间可控的前提下,与4种常见的一类分类(one-class classification,OCC)算法相比,所提方法可均衡地提高召回率、查准率和特异度,以LMK-OC-ELM-sig为代表,其在F1、曲线下方面积(area under curve,AUC)、G-mean和准确率4个指标上,比最近提出的局部多核异常检测(localized multiple kernel anomaly detection,LMKAD)方法分别提高了1.60％、1.57％、1.53％和2.23％.
Author 朱敏
刘奇
刘星
许晴
AuthorAffiliation 海军航空大学,山东烟台,264001%海军装备部,北京,100841%中国人民解放军92228部队,北京,100010
AuthorAffiliation_xml – name: 海军航空大学,山东烟台,264001%海军装备部,北京,100841%中国人民解放军92228部队,北京,100010
Author_FL ZHU Min
LIU Xing
LIU Qi
XU Qing
Author_FL_xml – sequence: 1
  fullname: ZHU Min
– sequence: 2
  fullname: LIU Qi
– sequence: 3
  fullname: LIU Xing
– sequence: 4
  fullname: XU Qing
Author_xml – sequence: 1
  fullname: 朱敏
– sequence: 2
  fullname: 刘奇
– sequence: 3
  fullname: 刘星
– sequence: 4
  fullname: 许晴
BookMark eNrjYmDJy89LZWBQNjTQM7Y0s9TP0sssLs7TMzQwMNQ1NTCL0DMyMDLQMzDTM7JkYeCEC3Mw8BYXZyYZmBoam5samJtwMlg9nb_rya4-H19vn6eTevyddV19fJ_PannRser5yl3Pp2x9unbCy-YVT3Zveza19eWsOc8WNzzb2v1s2s5nm6fyMLCmJeYUp_JCaW6GUDfXEGcPXR9_d09nRx_dYkMDU0td00TjJLPE5MQkkxTzRAvTlKTUJIs0yzSLZDOLZCMzCwNL8xRDw-TUNEMD80Rjc3PLFMukNBOgA1PTkizNDJNMjLkZtCDmlifmpSXmpcdn5ZcW5QFtjK8oSU-uTKnKKgb51sDMwMjSGADM4V_j
ClassificationCodes V243
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 10.3969/j.issn.1001-506X.2020.06.29
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 Fault detection method for avionics based on LMKL and OC-ELM
EndPage 1432
ExternalDocumentID xtgcydzjs202006029
GrantInformation_xml – fundername: 国家自然科学基金; 山东省自然科学基金资助课题
  funderid: (11802338); (ZR2017MF036)资助课题
GroupedDBID -0Y
2B.
4A8
5XA
5XJ
92E
92I
93N
ABJNI
ACGFS
ALMA_UNASSIGNED_HOLDINGS
CCEZO
CUBFJ
CW9
PSX
TCJ
TGP
U1G
U5S
ID FETCH-LOGICAL-s1059-5a3b6acab4d7a85dbeb8f9f8c68c268097d11cef107a3779d9bf4513efb961b43
ISSN 1001-506X
IngestDate Thu May 29 04:00:30 EDT 2025
IsPeerReviewed false
IsScholarly true
Issue 6
Keywords 局部多核学习
故障检测
一类分类
超限学习机
Language Chinese
LinkModel OpenURL
MergedId FETCHMERGED-LOGICAL-s1059-5a3b6acab4d7a85dbeb8f9f8c68c268097d11cef107a3779d9bf4513efb961b43
PageCount 9
ParticipantIDs wanfang_journals_xtgcydzjs202006029
PublicationCentury 2000
PublicationDate 2020-06-01
PublicationDateYYYYMMDD 2020-06-01
PublicationDate_xml – month: 06
  year: 2020
  text: 2020-06-01
  day: 01
PublicationDecade 2020
PublicationTitle 系统工程与电子技术
PublicationTitle_FL Systems Engineering and Electronics
PublicationYear 2020
Publisher 海军航空大学,山东烟台,264001%海军装备部,北京,100841%中国人民解放军92228部队,北京,100010
Publisher_xml – name: 海军航空大学,山东烟台,264001%海军装备部,北京,100841%中国人民解放军92228部队,北京,100010
SSID ssib051375074
ssib002263377
ssib001102898
ssib057620160
ssib023168126
ssib023646287
ssj0042237
Score 2.2573118
Snippet V243; 针对航空电子部件故障样本获取困难以及检测准确率不高的问题,提出基于局部多核学习(localized multiple kernel learning,LMKL)和一类超限学习机(one-class extreme learning...
SourceID wanfang
SourceType Aggregation Database
StartPage 1424
Title 基于LMKL和OC-ELM的航空电子部件故障检测方法
URI https://d.wanfangdata.com.cn/periodical/xtgcydzjs202006029
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnR1NaxQxNNQtiILiJ35TqDlOna9kEm-Z7ZSirR5sobcyM5mpeFjB3YL2JKggKCJiPRTBgxcVPEkF699pt_Zf-F4muzPiUqqXIbyXvM9M8l7IByFXcxgOpc64o3XJHYj_XSfjOnJ0VAZFlpdpqM1ui1t8djG8scSWxlrHGruWVnvZVL428lzJ_3gVYOBXPCX7D54dEgUAlMG_8AUPw_dAPqYJo3KGxoomIX5FMjd_c85AfSrat9sOAGgSUQm4kCaCCkGVQoiSphmgoCXDJmqaSpcmkoqAKmEoxjTmNOFUMioYooCObCNEBbhFAgrQVsSmDpSlgQS0etByEPMiFwACNSzEKDGwi0EGZiQRhgKwg0IySiROhbLsgLsa7kW2gNgbyNjAMFRVCkMFytEIDMevbLQB2yQoBWLAPGFzRcR3651bpg9b5UENpAgt4lH2Be4hVZFVRnHo0UZ5z0htdEaDRmj0yi4C_OlCNQghzfMGbDQjdACz9EVUu62iL9pURrZTqDYA8XKlsKJmmIIw2EvAwdO2GkrLkbX0DP0IWaAlAJsMWUtcymt0ElnZcB-2rt1PXE18uLWOueZVycE_35zY8EBkI0iCKNsfNQEHkkszASPRqSHRKXQT3pNrV7f-vOH8YW8lf6TX7nWxlstdXx4i4z6kfX6LjKvp-bk7dYCP8XBjgQCShyCoT1L7-AybVycU-DoC9-sEhHkBRMh1wgPJto9XLA5itxCCZfMc00Dww2TSanVtH53Mgb9OmXZWGrHpwgly3CaVE6oaIU6SsbW7p8jRxlWjp8n1nQ9b21uvcHzYefOyGhl2N57-ev5l9_PW7tvNna-v95582v75vb_-bG_jff_j4_7mi_67H_1v62fI4kyy0J517LspThezJYelQcbTPM1CHaWC6azIRClLkXOR-1y4MtKelxel50Yp3jeqZVaGYJqizCT3sjA4S1qd-53iHJlIA1eHMM-L1MPQ3xXaL3PNfJEVQQRz-XkyaTVftuNid_lvd144UK2L5Ej9N18ird6D1eIyRPy97IrtBr8BiGa1Yg
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%8ELMKL%E5%92%8COC-ELM%E7%9A%84%E8%88%AA%E7%A9%BA%E7%94%B5%E5%AD%90%E9%83%A8%E4%BB%B6%E6%95%85%E9%9A%9C%E6%A3%80%E6%B5%8B%E6%96%B9%E6%B3%95&rft.jtitle=%E7%B3%BB%E7%BB%9F%E5%B7%A5%E7%A8%8B%E4%B8%8E%E7%94%B5%E5%AD%90%E6%8A%80%E6%9C%AF&rft.au=%E6%9C%B1%E6%95%8F&rft.au=%E5%88%98%E5%A5%87&rft.au=%E5%88%98%E6%98%9F&rft.au=%E8%AE%B8%E6%99%B4&rft.date=2020-06-01&rft.pub=%E6%B5%B7%E5%86%9B%E8%88%AA%E7%A9%BA%E5%A4%A7%E5%AD%A6%2C%E5%B1%B1%E4%B8%9C%E7%83%9F%E5%8F%B0%2C264001%25%E6%B5%B7%E5%86%9B%E8%A3%85%E5%A4%87%E9%83%A8%2C%E5%8C%97%E4%BA%AC%2C100841%25%E4%B8%AD%E5%9B%BD%E4%BA%BA%E6%B0%91%E8%A7%A3%E6%94%BE%E5%86%9B92228%E9%83%A8%E9%98%9F%2C%E5%8C%97%E4%BA%AC%2C100010&rft.issn=1001-506X&rft.issue=6&rft.spage=1424&rft.epage=1432&rft_id=info:doi/10.3969%2Fj.issn.1001-506X.2020.06.29&rft.externalDocID=xtgcydzjs202006029
thumbnail_s http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fwww.wanfangdata.com.cn%2Fimages%2FPeriodicalImages%2Fxtgcydzjs%2Fxtgcydzjs.jpg