Impact force reconstruction and localization using nonconvex overlapping group sparsity

被引:50
作者
Liu, Junjiang [1 ,2 ]
Qiao, Baijie [1 ,2 ]
Chen, Yuanchang [3 ]
Zhu, Yuda [1 ,2 ]
He, Weifeng [1 ,2 ]
Chen, Xuefeng [1 ,2 ]
机构
[1] Xi An Jiao Tong Univ, Sch Mech Engn, Xian 710049, Peoples R China
[2] Xi An Jiao Tong Univ, State Key Lab Mfg Syst Engn, Xian 710049, Peoples R China
[3] Univ Massachusetts, Struct Dynam & Acoust Syst Lab, Lowell, MA 01854 USA
基金
中国国家自然科学基金;
关键词
Impact force identification; Under-determined problem; Nonconvex optimization; Group sparsity; IDENTIFICATION; DECONVOLUTION; REGULARIZATION; ALGORITHM;
D O I
10.1016/j.ymssp.2021.107983
中图分类号
TH [机械、仪表工业];
学科分类号
0802 ;
摘要
Although extensively studied, impact force identification is still a challenging task. When the location of the impact force is unknown, an under-determined problem is usually required to be tackled. In this paper, a novel impact force identification method based on the nonconvex overlapping group sparsity(NOGS) is proposed, allowing to localize the impact and recover its time history simultaneously from quite limited measurements(i.e., the number of responses is less than the number of potential impact locations). The NOGS not only enriches the prior information by taking the group sparsity structure of impact forces into consideration, but enhances the sparsity and the accuracy of estimated amplitude via its nonconvexity. A new algorithm, named fast nonconvex overlapping group sparsity algorithm(FaNogSa), derived in the light of the Majorize-Minimization(MM) principle is utilized to minimize the nonconvex objective function. Simulations and experiments are both implemented systematically on a stiffened composite structure to validate the proposed method, and two strain gauges are utilized to monitor 54 potential impacts. The corresponding results, comparing to the plain nonconvex(atan) regularization and the standard l1-norm regularization, say that the proposed method is able to localize the impact and at the same time recover its time history accurately, while under the same measuring conditions the nonconvex(atan) method and the l1-norm method usually fail.
引用
收藏
页数:19
相关论文
共 44 条
[1]  
Abrate S., 2005, Impact on Composite Structures
[2]   Sparsity-assisted bearing fault diagnosis using multiscale period group lasso [J].
An, Botao ;
Zhao, Zhibin ;
Wang, Shibin ;
Chen, Shaowen ;
Chen, Xuefeng .
ISA TRANSACTIONS, 2020, 98 :338-348
[3]   A space-frequency multiplicative regularization for force reconstruction problems [J].
Aucejo, M. ;
De Smet, O. .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2018, 104 :1-18
[4]   Bayesian source identification using local priors [J].
Aucejo, M. ;
De Smet, O. .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2016, 66-67 :120-136
[5]   A Fast Iterative Shrinkage-Thresholding Algorithm for Linear Inverse Problems [J].
Beck, Amir ;
Teboulle, Marc .
SIAM JOURNAL ON IMAGING SCIENCES, 2009, 2 (01) :183-202
[6]   Boundary Indicator for Aspect Limited Sensing of Hidden Dielectric Objects [J].
Bevacqua, Martina T. ;
Isernia, Tommaso .
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2018, 15 (06) :838-842
[7]   Shape Reconstruction via Equivalence Principles, Constrained Inverse Source Problems and Sparsity Promotion [J].
Bevacqua, Martina T. ;
Isernia, Tommaso .
PROGRESS IN ELECTROMAGNETICS RESEARCH-PIER, 2017, 158 :37-48
[8]   Study on solving the ill-posed problem of force load reconstruction [J].
Chang, Xiaotong ;
Yan, Yunju ;
Wu, Yafeng .
JOURNAL OF SOUND AND VIBRATION, 2019, 440 :186-201
[9]  
Chen G., 2018 IEEE INT S ANT, P697
[10]   Group-Sparse Signal Denoising: Non-Convex Regularization, Convex Optimization [J].
Chen, Po-Yu ;
Selesnick, Ivan W. .
IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2014, 62 (13) :3464-3478