A novel state space method for force identification based on the Galerkin weak formulation

Wang, Ting; Wan, Zhenzhen; Wang, Xuelin; Hu, Yujin

doi:10.1016/j.compstruc.2015.05.015

Cited by 23 publications

(11 citation statements)

References 36 publications

(41 reference statements)

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“…A noise of 5% level is added to the measured responses before identifying the input force. All parameters and boundary conditions of this numerical model are the same as the one solved by Wang et al. (2015).…”

Section: Numerical Simulationmentioning

confidence: 99%

“…FFGM is compared with the four methods, which are the Galerkin weak formulation (GW) (Wang et al., 2015), Tikhonov regularization method (TM), state space (Law and Yong, 2011), and Newmark (Liu et al., 2014) methods. The relative errors of the input forces identified by the five methods are shown in Table 4.…”

Section: Numerical Simulationmentioning

confidence: 99%

“…A forward model of dynamic load identification was established through the discretized convolution integral of loads and the corresponding unit-pulse response functions of the system. Wang et al. (2015) proposed a novel state space method that was extended to a refined version based on the discretization idea of the finite element method (FEM) to improve the identification accuracy.…”

Section: Introductionmentioning

confidence: 99%

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A novel method for force identification based on filter and FEM

Zheng

Wang

2019

Journal of Vibration and Control

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Force identification plays a vital role in vibration control and structural health monitoring. Force identification is an inverse problem to determine the excitation load from the responses. In general, the inverse problem has strong ill-posedness and ill-condition, especially in the case of low signal-to-noise ratio. Singular value decomposition and Tikhonov regularization are widely used for the reconstruction of the input force. Nevertheless, the anti-noise performance and accuracy of the two methods are still unsatisfactory. In this paper, we present a novel method for force identification based on a filter and the finite element method (FEM). The filter is used to suppress the noise of the input signals. FEM is introduced to reduce the discretization error and the regularization parameters are optimized by the genetic algorithm. After the signal is filtered, the characteristics of the signal will be changed. Especially, the initial and the last segments of the input signal will be lost. These changes may make the identified force inaccurate, even more inaccurate than the force identified with no filtering. To deal with this problem, cubic spline interpolation and a nonlinear autoregressive neural network are introduced to repair the input signals. Finally, three numerical examples and experiments are used to verify the superiority of the method.

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Section: Numerical Simulationmentioning

confidence: 99%

Section: Numerical Simulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A novel method for force identification based on filter and FEM

Zheng

Wang

2019

Journal of Vibration and Control

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“…Regularization-based methods mainly focus on finding a possible identified result, which can provide structural responses close to the measured ones as well as satisfy some prior information (Aucejo, 2014; Jacquelin et al, 2003; Li and Lu, 2016). Existing regularization methods used in the FR field include Tikhonov regularization (Li and Hao, 2016; Pan et al, 2017; Wang et al, 2015), truncate singular value decomposition (TSVD) (Lai et al, 2016), multiplicative regularization (Aucejo and De Smet, 2017), sparse regularization (Bao et al, 2016; Pan et al, 2018; Qiao et al, 2017; Rezayat et al, 2016), basis function methods (Qiao et al, 2015), and so on. In recent years, the sparse regularization has drawn lots of attentions (Hou et al, 2018a; Nagarajaiah and Yang, 2017; Yang and Nagarajaiah, 2017).…”

Section: Introductionmentioning

confidence: 99%

Identification of external forces via truncated response sparse decomposition under unknown initial conditions

Pan

2019

Advances in Structural Engineering

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When structural dynamic design for smart control and health monitoring is executed, imposed excitations on structures are one of most important issues in structural engineering. Identification of structural excitations, such as force reconstruction, moving force identification, and so on, has drawn increasing attentions in the last decades. Assumption of known initial conditions is a precondition for existing deterministic force reconstruction methods. However, initial conditions are often unknown and hard to be estimated. To address this problem, a novel truncated response sparse decomposition method is proposed for calculating the external forces under unknown initial conditions. The truncated response sparse decomposition involves two basic steps, that is, response sparse decomposition and force estimation. First, a collection of basis vectors is defined for expressing unknown forces. Unknown initial conditions are represented in modal space. Structural responses induced by both external forces and initial conditions are then normalized as potential response features to form an image dictionary, which is adapted to decompose the measured responses via sparse regularization. Second, the response features with lower decomposed amplitudes and small scaling factors are eliminated from the decomposed results. The remaining features are used for estimating the external forces. In order to assess the accuracy and the feasibility of the proposed force reconstruction method, some numerical simulations on a planar truss structure and a series of experimental studies are carried out. The illustrated results show the robustness and the applicability of the proposed method for addressing the force reconstruction problem under unknown initial conditions. Some related issues are discussed as well.

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“…Researchers in References [20,21] have discussed optimal arrangement of response sensors in dynamic loading identification processes through a large number of simulations and experiments. However, in actual engineering conditions, arrangement of response sensors is affected by structural design and external working environment [22,23].…”

Section: Introductionmentioning

confidence: 99%

Experimental Study of a Multipoint Random Dynamic Loading Identification Method Based on Weighted Average Technique

Gai¹,

Yu²

2019

Shock and Vibration

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Most of random dynamic loading identification research studies are about the original inverse pseudoexcitation method which does not fundamentally reduce the negative effect of ill-conditioned frequency response function matrix on accuracy of loading identification. This paper describes a new improved method based on weighted average technique to reduce peak errors between identified load spectrum and the actual load spectrum near some natural frequencies. Meanwhile, relative error of root mean square value between identified load and the actual load is reduced. The introduced selection method of threshold value is innovative which is the key of weighted average technique. This improved loading identification method is successfully applied to experiments of cantilever beam and thermal protection composite plate structure. Identification results prove that the proposed method is valid by good agreement between identified power spectrum density and the actual one. Moreover, this method has higher accuracy than inverse pseudoexcitation method in low-frequency band.

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A novel state space method for force identification based on the Galerkin weak formulation

Cited by 23 publications

References 36 publications

A novel method for force identification based on filter and FEM

A novel method for force identification based on filter and FEM

Identification of external forces via truncated response sparse decomposition under unknown initial conditions

Experimental Study of a Multipoint Random Dynamic Loading Identification Method Based on Weighted Average Technique

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