A probability density function discretization and approximation method for the dynamic load identification of stochastic structures

Liu, Jie; Sun, Xingsheng; Li, Kun; Jiang, Chao; Han, Xu

doi:10.1016/j.jsv.2015.07.022

Cited by 31 publications

(8 citation statements)

References 40 publications

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“…Dynamic load identification belongs to an ill-posedness of the inverse problem, which will lead to not useful solutions which cause large deviations from the exact solutions because of measured noise data and the randomness of structural parameters [5,6]. In recent years, much effort on solving this ill-posed problem has been devoted to overcoming the effects of structural uncertainty and measurement noise and improving the accuracy of dynamic load identification.…”

Section: Introductionmentioning

confidence: 99%

Research on Dynamic Load Identification Based on Explicit Wilson-θ and Improved Regularization Algorithm

Yuchuan

Zhao

2019

Shock and Vibration

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In the research of dynamic load identification, the method of obtaining kernel function matrix is usually rather cumbersome. To solve this problem, an explicit dynamic load identification algorithm based on the Wilson-θ (DLIAEW) method is proposed to easily obtain the kernel function matrix as long as the parameters of the system are known. To aim at the ill-posed problem in the inverse problem, this paper improves the Tikhonov regularization, proposes an improved regularization algorithm (IRA), and introduces the U-curve method to determine the regularization parameters. In the numeric simulation investigation of a four dofs vibrating system, effects of the sampling frequency and the noise level on the regularization parameters and the identification errors of impact and harmonic loads for the IRA are discussed in comparison with the Tikhonov regularization. Finally, the experiments of a cantilever beam excited by impact and harmonic loads are carried out to verify the advantages of the IRA.

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Section: Introductionmentioning

confidence: 99%

Research on Dynamic Load Identification Based on Explicit Wilson-θ and Improved Regularization Algorithm

Yuchuan

Zhao

2019

Shock and Vibration

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“…Generally speaking, based on the difference between present state of knowledge and complete knowledge, the uncertainty factors can be classified into two categories, ie, aleatory uncertainty and epistemic uncertainty . According to the sufficient sample statistical information, the aleatory uncertainty is usually quantified as random variable or stochastic process by probability theory . A lot of investigations have been conducted on the aleatory uncertainty‐based inverse analysis –.…”

Section: Introductionmentioning

confidence: 99%

“…10 According to the sufficient sample statistical information, the aleatory uncertainty is usually quantified as random variable or stochastic process by probability theory. 11 A lot of investigations have been conducted on the aleatory uncertainty-based inverse analysis. [12][13][14] Capasso et al described a framework for solving nonlinear inverse problems in a random environment and adopted the stochastic regularization approach to deal with scale-dependent modeling errors.…”

Section: Introductionmentioning

confidence: 99%

Novel interval theory‐based parameter identification method for engineering heat transfer systems with epistemic uncertainty

Wang

Matthies

2018

Numerical Meth Engineering

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Summary The parameter identification problem with epistemic uncertainty, where only a small amount of experimental information is available, is a challenging issue in engineering. To overcome the drawback of traditional probabilistic methods in dealing with limited data, this paper proposes a novel interval theory‐based inverse analysis method. First, the interval variables are introduced to represent the input uncertainties, whose lower and upper bounds are to be identified. Subsequently, an unbiased estimation method is presented to quantify the experimental response interval from limited measurements. Meanwhile, a quantitative metric is defined to characterize the relative errors between computational and experimental response intervals by which the interval parameter identification can be constructed as a nested‐loop optimization procedure. To improve the computational efficiency of response prediction with respect to various interval variables, a universal surrogate model is established in the support box via Legendre polynomial chaos expansion, where the expansion coefficients can be evaluated by a collocation method under Clenshaw‐Curtis points and Smolyak algorithm. Eventually, a heat conduction example is provided to verify the feasibility of proposed method, especially in the case with noise‐contaminated temperature measurements.

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“…Load identification is an important research area in structural health monitoring [ 1 , 2 , 3 , 4 ]. For reliability and cost effectiveness in the design and analysis of structures, accurate identification of the location and magnitude of a load is desirable.…”

Section: Introductionmentioning

confidence: 99%

Load Identification for a Cantilever Beam Based on Fiber Bragg Grating Sensors

Song

Zhang

Liang

2017

Sensors

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Load identification plays an important role in structural health monitoring, which aims at preventing structural failures. In order to identify load for linear systems and nonlinear systems, this paper presents methods to identify load for a cantilever beam based on dynamic strain measurement by Fiber Bragg Grating (FBG) sensors. For linear systems, the proposed inverse method consists of Kalman filter with no load terms and a linear estimator. For nonlinear systems, the proposed inverse method consists of cubature Kalman filter (CKF) with no load terms and a nonlinear estimator. In the process of load identification, the state equations of the beam structures are constructed by using the finite element method (FEM). Kalman filter or CKF is used to suppress noise. The residual innovation sequences, gain matrix, and innovation covariance generated by Kalman filter or CKF are used to identify a load. To prove the effectiveness of the proposed method, numerical simulations and experiments of the beam structures are employed and the results show that the method has an excellent performance.

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A probability density function discretization and approximation method for the dynamic load identification of stochastic structures

Cited by 31 publications

References 40 publications

Research on Dynamic Load Identification Based on Explicit Wilson-θ and Improved Regularization Algorithm

Research on Dynamic Load Identification Based on Explicit Wilson-θ and Improved Regularization Algorithm

Novel interval theory‐based parameter identification method for engineering heat transfer systems with epistemic uncertainty

Load Identification for a Cantilever Beam Based on Fiber Bragg Grating Sensors

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