Identification of Time-Varying External Force Using Group Sparse Regularization and Redundant Dictionary

被引:1
作者
Liu, Huanlin [1 ,2 ,3 ]
Ma, Hongwei [1 ,2 ]
机构
[1] Dongguan Univ Technol, Sch Environm & Civil Engn, Dongguan 523808, Peoples R China
[2] Guangdong Prov Key Lab Intelligent Disaster Preven, Dongguan 523808, Peoples R China
[3] Xi An Jiao Tong Univ, Sch Aerosp, Xian 710049, Peoples R China
基金
国家重点研发计划;
关键词
time-varying external force identification; inverse problems in vibration; group sparse regularization; redundant dictionary; standard l(1)-norm regularization; function expansion method; RECONSTRUCTION; ALGORITHM; STRATEGY;
D O I
10.3390/s23010151
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
How to accurately identify unknown time-varying external force from measured structural responses is an important engineering problem, which is critical for assessing the safety condition of the structure. In the context of a few available accelerometers, this paper proposes a novel time-varying external force identification method using group sparse regularization based on the prior knowledge in the redundant dictionary. Firstly, the relationship between time-varying external force and acceleration responses is established, and a redundant dictionary is designed to create a sparse expression of external force. Then, the relevance of atoms in the redundant dictionary is revealed, and this prior knowledge is used to determine the group structures of atoms. As a result, a force identification governing equation is formulated, and the group sparse regularization is reasonably introduced to ensure the accuracy of the identified results. The contribution of this paper is that the group structures of atoms are reasonably determined based on prior knowledge, and the complexity in the process for identifying external force from measured acceleration responses is reduced. Finally, the effectiveness of the proposed method is demonstrated by numerical simulations and an experimental structure. The illustrated results show that, compared with the force identification method based on the standard l(1)-norm regularization, the proposed method can further improve the identified accuracy of unknown external force and greatly enhance the computational efficiency for the force identification problem.
引用
收藏
页数:19
相关论文
共 39 条
  • [1] A novel algorithm for solving multiplicative mixed-norm regularization problems
    Aucejo, M.
    De Smet, O.
    [J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2020, 144
  • [2] An iterated multiplicative regularization for force reconstruction problems
    Aucejo, M.
    De Smet, O.
    [J]. JOURNAL OF SOUND AND VIBRATION, 2018, 437 : 16 - 28
  • [3] Sparse l1 optimization-based identification approach for the distribution of moving heavy vehicle loads on cable-stayed bridges
    Bao, Yuequan
    Li, Hui
    Chen, Zhicheng
    Zhang, Fujian
    Guo, Anxin
    [J]. STRUCTURAL CONTROL & HEALTH MONITORING, 2016, 23 (01) : 144 - 155
  • [4] A truncated generalized singular value decomposition algorithm for moving force identification with ill-posed problems
    Chen, Zhen
    Chan, Tommy H. T.
    [J]. JOURNAL OF SOUND AND VIBRATION, 2017, 401 : 297 - 310
  • [5] Group Relevance Vector Machine for sparse force localization and reconstruction
    Feng, Wei
    Li, Qiaofeng
    Lu, Qiuhai
    Li, Chen
    Wang, Bo
    [J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2021, 161
  • [6] Regularization tools version 4.0 for matlab 7.3
    Hansen, Per Christian
    [J]. NUMERICAL ALGORITHMS, 2007, 46 (02) : 189 - 194
  • [7] Inoue H., 2001, Applied Mechanics Reviews, V54, P503, DOI DOI 10.1115/1.1420194
  • [8] Force reconstruction: analysis and regularization of a deconvolution problem
    Jacquelin, E
    Bennani, A
    Hamelin, P
    [J]. JOURNAL OF SOUND AND VIBRATION, 2003, 265 (01) : 81 - 107
  • [9] Reliability-based design optimization of structural systems under stochastic excitation: An overview
    Jerez, D. J.
    Jensen, H. A.
    Beer, M.
    [J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2022, 166
  • [10] Soft computing methods for fatigue life estimation: A review of the current state and future trends
    Kalayci, Can B.
    Karagoz, Sevcan
    Karakas, Ozler
    [J]. FATIGUE & FRACTURE OF ENGINEERING MATERIALS & STRUCTURES, 2020, 43 (12) : 2763 - 2785