Force localization and reconstruction using a two-step iterative approach

Li, Qiaofeng; Lu, Qiming

doi:10.1177/1077546317713366

Cited by 20 publications

(8 citation statements)

References 22 publications

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“…As shown in equation (22), the ill-posed factor of the load identification model is controlled by using the smaller singular value σ i rather than the maximal singular value σ 1 . With the singular values going down to zero, the error of measurement responses would strongly influence the stability of identified results.…”

Section: Regularization Methodmentioning

confidence: 99%

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Load Localization and Reconstruction Using a Variable Separation Method

Zhang

Jiang

2019

Shock and Vibration

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Load identification is very important in engineering practice. In this paper, a novel method for load reconstruction and localization is proposed. In the traditional load localization method, location information is coupled to the impulse response matrix. The inversion of the impulse response matrix leads the process of load localization to be time-consuming. So we propose a variable separation method to separate the load location information from the impulse response matrix. An error optimization function of load histories in different modes is employed to determine the true load location. After locating the external load, the load time history can be easily reconstructed by the measurement responses and determinate impulse response matrix. This method is verified by simulations of a simply supported beam acted by a sine load and an impact separately. An experiment is also carried out to validate the feasibility and accuracy of the proposed method.

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Section: Regularization Methodmentioning

confidence: 99%

“…ey [22] used a two-step iterative approach to both localize and reconstruct a single point force acting on a structure. In their research, they proposed the stabilization diagram of identified locations to determine the appropriate regularization range and the true force location.…”

Section: Introductionmentioning

confidence: 99%

Load Localization and Reconstruction Using a Variable Separation Method

Zhang

Jiang

2019

Shock and Vibration

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“…Formulations that aim at simultaneous identification of the locations and time-dependent magnitudes of the unknown forces are rare. A two-step iterative procedure for localization and identification of a single force is proposed in [ 28 ]. Force localization is based on a stabilization diagram computed for the regularization parameter, but the assumption of a single stationary force precludes direct application in the case of moving loads.…”

Section: Introductionmentioning

confidence: 99%

Trajectory Identification for Moving Loads by Multicriterial Optimization

Gawlicki

Jankowski

2021

Sensors

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Moving load is a fundamental loading pattern for many civil engineering structures and machines. This paper proposes and experimentally verifies an approach for indirect identification of 2D trajectories of moving loads. In line with the “structure as a sensor” paradigm, the identification is performed indirectly, based on the measured mechanical response of the structure. However, trivial solutions that directly fit the mechanical response tend to be erratic due to measurement and modeling errors. To achieve physically meaningful results, these solutions need to be numerically regularized with respect to expected geometric characteristics of trajectories. This paper proposes a respective multicriterial optimization framework based on two groups of criteria of a very different nature: mechanical (to fit the measured response of the structure) and geometric (to account for the geometric regularity of typical trajectories). The state-of-the-art multiobjective genetic algorithm NSGA-II is used to find the Pareto front. The proposed approach is verified experimentally using a lab setup consisting of a plate instrumented with strain gauges and a line-follower robot. Three trajectories are tested, and in each case the determined Pareto front is found to properly balance between the mechanical response fit and the geometric regularity of the trajectory.

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“…The iteration step is controlled by L‐curve criterion. The application of regularization techniques could be found in other literatures …”

Section: Introductionmentioning

confidence: 99%

A revised time domain force identification method based on Bayesian formulation

2019

Numerical Meth Engineering

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Time domain force identification problems are generally ill-posed. Regularization techniques are widely adopted to well-condition the problem. Traditional regularization such as Tikhonov regularization is mathematically equivalent to assuming a zero-mean prior distribution on the unknown force. This assumption could be unreasonable for problems where notable trend components exist in force histories, such as vehicle-bridge moving force identification and cutting tool force identification. In this paper, a revised method is proposed to address this issue. The proposed method formulates the force identification problem within the Bayesian framework. The trend components are considered as low-order polynomials and as the mean term in the prior distribution of the force history. The joint maximum a posterior estimate of unknown variables is derived. The solution algorithm is given based on conditional maximization. A mass-spring system and a cutting tool under forces containing various types of trend components and vehicle-bridge systems under moving interaction forces are simulated to validate the proposed method.

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Force localization and reconstruction using a two-step iterative approach

Cited by 20 publications

References 22 publications

Load Localization and Reconstruction Using a Variable Separation Method

Load Localization and Reconstruction Using a Variable Separation Method

Trajectory Identification for Moving Loads by Multicriterial Optimization

A revised time domain force identification method based on Bayesian formulation

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