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Study of cumulative fatigue damage detection for used parts with nonlinear output frequency response functions based on NARMAX modelling

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成果类型:
期刊论文
作者:
Huang, Honglan;Mao, Hanying;Mao, Hanling*;Zheng, Weixue;Huang, Zhenfeng;...
通讯作者:
Mao, Hanling
作者机构:
[Li, Xinxin; Mao, Hanling; Huang, Zhenfeng; Huang, Honglan; Zheng, Weixue] Guangxi Univ, Coll Mech Engn, Nanning 530004, Peoples R China.
[Mao, Hanying] Guangxi Univ Sci & Technol, Coll Automobile & Transportat, Liuzhou 542506, Peoples R China.
[Wang, Xianghong] Changsha Univ Sci & Technol, Sch Automobile & Mech Engn, Changsha 410004, Hunan, Peoples R China.
通讯机构:
[Mao, Hanling] G
Guangxi Univ, Coll Mech Engn, Nanning 530004, Peoples R China.
语种:
英文
关键词:
Composite structures;Damage detection;Frequency response;Cumulative fatigue damage;Engineering applications;NARMAX model;NOFRFs;Non-linear output frequency response function;Nonlinear properties;Rectangular pulse excitations;Used parts;Fatigue damage
期刊:
Journal of Sound and Vibration
ISSN:
0022-460X
年:
2017
卷:
411
页码:
75-87
基金类别:
The authors gratefully acknowledge that the study was supported by the projects of the National Natural Science Foundation of China ( 51365006 , 51445013 and 51105045 ), the project of the Guangxi Natural Science Foundation ( 2016GXNSFAA380119 ) and projects of the Guangxi Key Laboratory for Manufacturing Systems and Advanced Manufacturing Technology ( 14-045-15S05 ).
机构署名:
本校为其他机构
院系归属:
汽车与机械工程学院
摘要:
Cumulative fatigue damage detection for used parts plays a key role in the process of remanufacturing engineering and is related to the service safety of the remanufactured parts. In light of the nonlinear properties of used parts caused by cumulative fatigue damage, the based nonlinear output frequency response functions detection approach offers a breakthrough to solve this key problem. First, a modified PSO-adaptive lasso algorithm is introduced to improve the accuracy of the NARMAX model under impulse hammer excitation, and then, an effective new algorithm is derived to estimate the nonlin...

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