基于数据融合的疲劳寿命预测方法

V21%TB302; 针对疲劳实验耗时长、实验数据分散性大,通过小样本数据获得的高存活率P-S-N曲线不够准确,疲劳寿命预测不够准确和可靠的问题,基于性能-寿命概率映射原理数据融合方法对不同应力级的小样本疲劳数据进行数据融合,并分析和评估通过该方法获得准确P-S-N曲线的可行性.与融合前的小样本疲劳数据相比,数据融合后所得P-S-N曲线更接近总体大样本数据得出的P-S-N曲线,表明该方法能够在减少疲劳实验量的前提下有效提高疲劳寿命预测的可靠性与准确性.对比和评价不同模型对融合前与融合后数据的寿命预测能力,发现三参数幂函数模型的预测能力较强,而对于大样本数据,四种模型(Basquin S-N模型...

Full description

Saved in:
Bibliographic Details
Published in航空材料学报 Vol. 44; no. 6; pp. 107 - 115
Main Authors 张旭, 姚建尧, 刘许旸, 王常印, 高友智
Format Journal Article
LanguageChinese
Published 重庆大学航空航天学院,重庆 400044 01.12.2024
Subjects
fatigue
life prediction
寿命预测
data augmentation
model comparison
模型对比
stress life model
数据融合
应力寿命模型
疲劳
Online AccessGet full text
ISSN1005-5053
DOI10.11868/j.issn.1005-5053.2023.000017

Cover

Abstract V21%TB302; 针对疲劳实验耗时长、实验数据分散性大,通过小样本数据获得的高存活率P-S-N曲线不够准确,疲劳寿命预测不够准确和可靠的问题,基于性能-寿命概率映射原理数据融合方法对不同应力级的小样本疲劳数据进行数据融合,并分析和评估通过该方法获得准确P-S-N曲线的可行性.与融合前的小样本疲劳数据相比,数据融合后所得P-S-N曲线更接近总体大样本数据得出的P-S-N曲线,表明该方法能够在减少疲劳实验量的前提下有效提高疲劳寿命预测的可靠性与准确性.对比和评价不同模型对融合前与融合后数据的寿命预测能力,发现三参数幂函数模型的预测能力较强,而对于大样本数据,四种模型(Basquin S-N模型、指数S-N模型、三参数幂函数S-N模型(基于对数正态分布)、三参数幂函数S-N模型(基于三参数威布尔分布))的预测能力很接近.
AbstractList V21%TB302; 针对疲劳实验耗时长、实验数据分散性大,通过小样本数据获得的高存活率P-S-N曲线不够准确,疲劳寿命预测不够准确和可靠的问题,基于性能-寿命概率映射原理数据融合方法对不同应力级的小样本疲劳数据进行数据融合,并分析和评估通过该方法获得准确P-S-N曲线的可行性.与融合前的小样本疲劳数据相比,数据融合后所得P-S-N曲线更接近总体大样本数据得出的P-S-N曲线,表明该方法能够在减少疲劳实验量的前提下有效提高疲劳寿命预测的可靠性与准确性.对比和评价不同模型对融合前与融合后数据的寿命预测能力,发现三参数幂函数模型的预测能力较强,而对于大样本数据,四种模型(Basquin S-N模型、指数S-N模型、三参数幂函数S-N模型(基于对数正态分布)、三参数幂函数S-N模型(基于三参数威布尔分布))的预测能力很接近.
Abstract_FL To address the challenges posed by the time-consuming nature of fatigue test and the scattered nature of test data,it is evident that P-S-N curves derived from small samples with high survival rates lack sufficient accuracy,leading to unreliable predictions of fatigue life.The data fusion method based on the performance-life probability mapping principle is used to fuse small sample fatigue data of different stress levels,and the feasibility of obtaining accurate P-S-N curves by this method is analyzed and evaluated.The results demonstrated that P-S-N curves obtained post-fusion are closer to the P-S-N curve derived from larger sample datasets.This approach effectively enhances both reliability and accuracy in predicting fatigue life while simultaneously reducing the amount of required fatigue tests.A comparative evaluation is conducted on the predictive capabilities for fatigue life before and after fusion using different models;notably,it is found that the three-parameter power function model demonstrates superior predictive ability,whereas when ample fatigue data is available,the prediction capabilities among four models(Basquin S-N model,exponential S-N model,three-parameter power function S-N model(based on lognormal distribution),and three-parameter power function S-Nmodel(based on three-parameter Weibull distribution)exhibit a considerable degree of resemblance.
Author 高友智
姚建尧
刘许旸
张旭
王常印
AuthorAffiliation 重庆大学航空航天学院,重庆 400044
AuthorAffiliation_xml – name: 重庆大学航空航天学院,重庆 400044
Author_FL ZHANG Xu
YAO Jianyao
GAO Youzhi
LIU Xuyang
WANG Changyin
Author_FL_xml – sequence: 1
  fullname: ZHANG Xu
– sequence: 2
  fullname: YAO Jianyao
– sequence: 3
  fullname: LIU Xuyang
– sequence: 4
  fullname: WANG Changyin
– sequence: 5
  fullname: GAO Youzhi
Author_xml – sequence: 1
  fullname: 张旭
– sequence: 2
  fullname: 姚建尧
– sequence: 3
  fullname: 刘许旸
– sequence: 4
  fullname: 王常印
– sequence: 5
  fullname: 高友智
BookMark eNo9j7tKA0EYhaeIYIx5DLHa9Z_r7pYSvEHARuvw786OJoYJOIg-QApRIlokhYWCD6BisDAgvowz0bdwQfE0B77iO5wlUrMDWxKyQiGmNFXpWi_uOmdjCiAjCZLHDBiPoQpNaqT-zxdJ07luXmHKslSmdRL5-9nn7CqMn8Po8etu5K_P57fD-eTFX0z904e_ef9-GIbXyzB5C9PxMlkw2Hdl868bZH9zY6-1HbV3t3Za6-3IUWAsEjorlOZaZIYpKSgmqA0UMjeoUQiTcMiBa2S6xCKnslSYKaZEqZJMM0DeIKu_3lO0Bu1Bpzc4ObbVYufwqOif5dU9AQoo4z8MUlmp
ClassificationCodes V21%TB302
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 10.11868/j.issn.1005-5053.2023.000017
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 Fatigue life prediction method based on data fusion
EndPage 115
ExternalDocumentID hkclxb202406012
GrantInformation_xml – fundername: (国家重点研发计划); (国家科技重大专项)
  funderid: (国家重点研发计划); (国家科技重大专项)
GroupedDBID -03
2B.
4A8
5VS
5XA
5XC
5XD
92H
92I
93N
ABJNI
ACGFS
ADMLS
ALMA_UNASSIGNED_HOLDINGS
CCEZO
CEKLB
CW9
FIJ
GROUPED_DOAJ
IPNFZ
PSX
RIG
TCJ
TGT
U1G
U5L
U5M
ID FETCH-LOGICAL-s1022-4d9c6d3d49f26541a7adf0c5bfada44f730b03da2deacb15e6a96264e679d20a3
ISSN 1005-5053
IngestDate Thu May 29 04:00:13 EDT 2025
IsPeerReviewed true
IsScholarly true
Issue 6
Keywords fatigue
life prediction
寿命预测
data augmentation
model comparison
模型对比
stress life model
数据融合
应力寿命模型
疲劳
Language Chinese
LinkModel OpenURL
MergedId FETCHMERGED-LOGICAL-s1022-4d9c6d3d49f26541a7adf0c5bfada44f730b03da2deacb15e6a96264e679d20a3
PageCount 9
ParticipantIDs wanfang_journals_hkclxb202406012
PublicationCentury 2000
PublicationDate 2024-12-01
PublicationDateYYYYMMDD 2024-12-01
PublicationDate_xml – month: 12
  year: 2024
  text: 2024-12-01
  day: 01
PublicationDecade 2020
PublicationTitle 航空材料学报
PublicationTitle_FL Journal of Aeronautical Materials
PublicationYear 2024
Publisher 重庆大学航空航天学院,重庆 400044
Publisher_xml – name: 重庆大学航空航天学院,重庆 400044
SSID ssib001129858
ssib023167179
ssib051375391
ssj0000561693
ssib038074666
ssib031741046
Score 2.4409473
Snippet V21%TB302; 针对疲劳实验耗时长、实验数据分散性大,通过小样本数据获得的高存活率P-S-N曲线不够准确,疲劳寿命预测不够准确和可靠的问题,基于性能-寿命概率映射原理数据融合...
SourceID wanfang
SourceType Aggregation Database
StartPage 107
Title 基于数据融合的疲劳寿命预测方法
URI https://d.wanfangdata.com.cn/periodical/hkclxb202406012
Volume 44
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
journalDatabaseRights – providerCode: PRVAON
  databaseName: DOAJ Directory of Open Access Journals
  issn: 1005-5053
  databaseCode: DOA
  dateStart: 20120101
  customDbUrl:
  isFulltext: true
  dateEnd: 99991231
  titleUrlDefault: https://www.doaj.org/
  omitProxy: true
  ssIdentifier: ssj0000561693
  providerName: Directory of Open Access Journals
– providerCode: PRVEBS
  databaseName: Inspec with Full Text
  issn: 1005-5053
  databaseCode: ADMLS
  dateStart: 20190201
  customDbUrl:
  isFulltext: true
  dateEnd: 99991231
  titleUrlDefault: https://www.ebsco.com/products/research-databases/inspec-full-text
  omitProxy: false
  ssIdentifier: ssj0000561693
  providerName: EBSCOhost
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV3Ni9QwFA_rCqIH8RO_mYM5Scc2TdPkmM50WAQFYRf2tqRN64IygjsLsuc9iLKih92DBwX_ABUXDy6I_4wzq_-F76WdmTIjfl1KJvn1fc7Me2mTF0KuC2syFZbWK1huPF5K7ikjMi-28OXwYUYRGNzvfPuOWFrht1aj1YUjdxurljYHWTvf-uW-kv_xKvSBX3GX7D94dkIUOqAN_oUreBiuf-VjmkZU9WiiacrxKlOaCqoimvjYkF2qoUdSlWIbwT6VkqYxVQDmriFownBIapqE2NBAsOfAAU3gLkU1c2BAAixxLKCtXE9Iq9MrxwkusgMWWiNxrZxsgO8i6-pGpRwXkE04ITXV0djtOJJ0qK6wQKDbHNFOboQkjq7TU8dNCLBWEmUAxRNZU0nkFBKjkVCJCMcREqFtEr_5-IPxmaUkaAUZ10ZEQwsnD0eRpsrMaz7pqcBNzRUaAizLOvPEb3D3CrwRMLCSK2SRYTOiTAEz4aE-4bfONIJqI-t8EJNCuiiGHNoTDm086d7V2wziafSerKlcv58_eJyhibDEDmQlR1ksBGs8ZXAZMuR3jfJFDAshBNMXtZBNcnzrP_mMtZIaM9woCGF-W5dXqsrnCyzn49YU1HIeI3Ssxc3f6eC2yfVL07_XyOiWT5GT9VSspavf1WmysLV-hpxoFOg8S7zhm4NvB89Hux9GO---v94Zvnhy-Gr7cO_j8On-8P3X4csvP95ujz49G-19Hu3vniMrvXS5s-TVB4x4G_igw-NW5cKGlquSiYgHJja29PMoK401nJcQ_cAE1jAL6UkWRIUwSsAMohCxssw34Xmy2H_YLy6QlmR5ofwS5v8GYqItVR4qaVlsiiIuZFleJK1a2bX6D2RjbcZjl_4MuUyOT38DV8ji4NFmcRWS4kF2zbn5J4HFhH4
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%8E%E6%95%B0%E6%8D%AE%E8%9E%8D%E5%90%88%E7%9A%84%E7%96%B2%E5%8A%B3%E5%AF%BF%E5%91%BD%E9%A2%84%E6%B5%8B%E6%96%B9%E6%B3%95&rft.jtitle=%E8%88%AA%E7%A9%BA%E6%9D%90%E6%96%99%E5%AD%A6%E6%8A%A5&rft.au=%E5%BC%A0%E6%97%AD&rft.au=%E5%A7%9A%E5%BB%BA%E5%B0%A7&rft.au=%E5%88%98%E8%AE%B8%E6%97%B8&rft.au=%E7%8E%8B%E5%B8%B8%E5%8D%B0&rft.date=2024-12-01&rft.pub=%E9%87%8D%E5%BA%86%E5%A4%A7%E5%AD%A6%E8%88%AA%E7%A9%BA%E8%88%AA%E5%A4%A9%E5%AD%A6%E9%99%A2%2C%E9%87%8D%E5%BA%86+400044&rft.issn=1005-5053&rft.volume=44&rft.issue=6&rft.spage=107&rft.epage=115&rft_id=info:doi/10.11868%2Fj.issn.1005-5053.2023.000017&rft.externalDocID=hkclxb202406012
thumbnail_s http://utb.summon.serialssolutions.com/2.0.0/image/custom?url=http%3A%2F%2Fwww.wanfangdata.com.cn%2Fimages%2FPeriodicalImages%2Fhkclxb%2Fhkclxb.jpg