Indirect measurement of the impulsive load to a nonlinear system from dynamic responses: Inverse problem formulation

Jang, Taichang; Baek, Hyoungsu; Han, S. L.; Kinoshita, Takeshi

doi:10.1016/j.ymssp.2010.01.003

Cited by 48 publications

(23 citation statements)

References 25 publications

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“…Jacquelin et al dealt with the identification of the impact force on the aluminum plate, when they evaluated several methods for improving of the task conditionality (so-called regularization methods) and showed their parameter setting [2]. Jang et al dealt with the general solvability of the inverse task for impact loads and performed numerical simulation for selected tasks using the Tikhonov and Landwerber-Fridmen regularization [3]. Furthermore, Kim and Lee have considered using the singular value decomposition to improve conditionality of the task and the method was verified experimentally on cantilever beam [4].…”

Section: Theorymentioning

confidence: 99%

Numerical simulation and measurement of car strut under shock vibration

Novák¹,

Petrů²,

Lufinka³

2017

Vib. proced.

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Abstract. This article focuses on the analysis of the properties of the car strut used to capture the engine torque reaction generated during fast acceleration. There is described the methodology of measuring the parameters needed to assess the force effect and also a simulation of the properties of a redesigned strut using the finite element method. The simulation confirmed the correctness of the design and also showed that very small asymmetry of cross section cause significant reducing of the load capacity of the strut and its easy damage.

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Section: Theorymentioning

confidence: 99%

Numerical simulation and measurement of car strut under shock vibration

Novák¹,

Petrů²,

Lufinka³

2017

Vib. proced.

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“…where C † is the Moore-Penrose pseudoinverse of C which is usually an ill-conditioned operator, as, for example, it could be if det = 0, which causes (2) to be a typical ill-posed problem; see [1,2].…”

Section: Introductionmentioning

confidence: 99%

A Novel Fractional Tikhonov Regularization Coupled with an Improved Super-Memory Gradient Method and Application to Dynamic Force Identification Problems

Wang

Ren

Liu

2018

Mathematical Problems in Engineering

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This paper presents a novel inverse technique to provide a stable optimal solution for the ill-posed dynamic force identification problems. Due to ill-posedness of the inverse problems, conventional numerical approach for solutions results in arbitrarily large errors in solution. However, in the field of engineering mathematics, there are famous mathematical algorithms to tackle the illposed problem, which are known as regularization techniques. In the current study, a novel fractional Tikhonov regularization (NFTR) method is proposed to perform an effective inverse identification, then the smoothing functional of the ill-posed problem processed by the proposed method is regarded as an optimization problem, and finally a stable optimal solution is obtained by using an improved super-memory gradient (ISMG) method. The result of the present method is compared with that of the standard TR method and FTR method; new findings can be obtained; that is, the present method can improve accuracy and stability of the inverse identification problem, robustness is stronger, and the rate of convergence is faster. The applicability and efficiency of the present method in this paper are demonstrated through a mathematical example and an engineering example.

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“…Aiming to reduce the above problems, it is necessary to obtain a method for solving ill-conditioned problem. Accordingly, regularization techniques, which can treat the ill-conditioned problem, have been utilized in many fields [10][11][12][13]. In the past few years, maximum entropy (ME) regularization techniques have been put forward one after another, which has been applied successfully in the wide areas of image reconstruction, signal processing, force identification problem, and so on [14], and the advantages of using ME regularization technique are as follows: firstly, some important information from the incomplete data can be extracted; then the probability distribution of the constraint is hidden in it; and, finally, entropy term can be changed according to different objects [15].…”

Section: Introductionmentioning

confidence: 99%

Identification of Random Dynamic Force Using an Improved Maximum Entropy Regularization Combined with a Novel Conjugate Gradient

Ren

Wang

Liu

2017

Mathematical Problems in Engineering

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We propose a novel mathematical algorithm to offer a solution for the inverse random dynamic force identification in practical engineering. Dealing with the random dynamic force identification problem using the proposed algorithm, an improved maximum entropy (IME) regularization technique is transformed into an unconstrained optimization problem, and a novel conjugate gradient (NCG) method was applied to solve the objective function, which was abbreviated as IME-NCG algorithm. The result of IME-NCG algorithm is compared with that of ME, ME-CG, ME-NCG, and IME-CG algorithm; it is found that IME-NCG algorithm is available for identifying the random dynamic force due to smaller root mean-square-error (RMSE), lower restoration time, and fewer iterative steps. Example of engineering application shows that L-curve method is introduced which is better than Generalized Cross Validation (GCV) method and is applied to select regularization parameter; thus the proposed algorithm can be helpful to alleviate the ill-conditioned problem in identification of dynamic force and to acquire an optimal solution of inverse problem in practical engineering.

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Indirect measurement of the impulsive load to a nonlinear system from dynamic responses: Inverse problem formulation

Cited by 48 publications

References 25 publications

Numerical simulation and measurement of car strut under shock vibration

Numerical simulation and measurement of car strut under shock vibration

A Novel Fractional Tikhonov Regularization Coupled with an Improved Super-Memory Gradient Method and Application to Dynamic Force Identification Problems

Identification of Random Dynamic Force Using an Improved Maximum Entropy Regularization Combined with a Novel Conjugate Gradient

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