Identification of Dynamic Parameters of a Running Load and Beam System

Maruyama, Osamu; Saito, Etsuro; Hoshiya, Masaru

doi:10.2208/jscej.1986.368_283

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…After some manipulations, Eq, (13) reduces to (14) The above formula gives a set of linear simultaneous equations for A;1, which can be solved easily.…”

Section: J-fmentioning

confidence: 99%

Sensitivities of Parameters Due to Model Errors and Measurement Noises in Structural Identification Problems

Matsui

Kurita

1990

Doboku Gakkai Ronbunshu

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This paper deals with the development of sensitivities of structural parameter estimates with respect to model errors and measurement noises. Structural identification algorithm is normally developed based on the concept of least square method. Then the determination of parameters is treated as an optimization problem and the parameter estimates are an optimum solution of the problem. Hence the sensitivities of the parameters with respect to errors and noises can be formulated in the manner similar to sensitivities of optimum solutions. Numerical examples are presented to demonstrate an effectiveness of the method proposed.

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“…After some manipulations, Eq, (13) reduces to (14) The above formula gives a set of linear simultaneous equations for A;1, which can be solved easily.…”

Section: J-fmentioning

confidence: 99%

Sensitivities of Parameters Due to Model Errors and Measurement Noises in Structural Identification Problems

Matsui

Kurita

1990

Doboku Gakkai Ronbunshu

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“…For the abovementioned reasons, algorithms for simultaneously identifying structural parameters and loads have been developed. Maruyama and Hoshiya et al [12,13] described a moving load by several parameters, such as the moving speed and the load size, and introduced these parameters into the structural state equation; subsequently, the extended Kalman filter (EKF) was used for the identification of loads and parameters. Yang et al [14] proposed arecursive least-squares estimation with unknown input (RLSE-UI) algorithm to identify unknown parameters and input.…”

Section: Introductionmentioning

confidence: 99%

An Improved Sensitivity Method for the Simultaneous Identification of Unknown Parameters and External Loads of Nonlinear Structures

Wang

2018

Shock and Vibration

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Because structures may be subject to unknown loads and may simultaneously involve unknown parameters and because simple load identification or parameter identification algorithms cannot be applied under such conditions, it is necessary to seek algorithms that can simultaneously identify unknown parameters and external loads of structures. The sensitivity method is one of them, and this paper extends this method to nonlinear structures. In addition, the key issues associated with the sensitivity method are systematically studied, and suggestions for improvement are put forward, including the use of the difference method instead of the derivative method to calculate the sensitivity, the use of a fixed regularization parameter instead of the traditional regularization parameter calculation methods, and measures for guarantee of iterative convergence. The improved sensitivity method is applied to two types of nonlinear structures, and the effects of the regularization parameter, distribution of measured points, response types, noise levels, and the magnitude of the perturbation on the identified results are discussed.

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An improved Kalman filter with dummy measurement for identification of structural load and unknown parameters

Wang,

Du,

Ghaemian

et al. 2024

Journal of Sound and Vibration

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Identification of Dynamic Parameters of a Running Load and Beam System

Cited by 3 publications

References 11 publications

Sensitivities of Parameters Due to Model Errors and Measurement Noises in Structural Identification Problems

Sensitivities of Parameters Due to Model Errors and Measurement Noises in Structural Identification Problems

An Improved Sensitivity Method for the Simultaneous Identification of Unknown Parameters and External Loads of Nonlinear Structures

An improved Kalman filter with dummy measurement for identification of structural load and unknown parameters

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