Input force identification using Kalman filter techniques: application to soil-pile interaction

Loh, Chin‐Hsiung; Wu, Ai-Lun; Yang, Jann N.; Chen, Chia-Han; Ueng, Tzuu‐Huei

doi:10.1117/12.773816

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…A shape function method combined with the moving least-square scheme was developed to study its performance in load identification, particularly, its capability of reducing the ill-posedness involved in reconstructing loads (Liu et al (2014)). In Loh et al (2008), the time-varying excitation force identification was studied, where the Kalman filter was employed to make a regression model between the excitation force and the residual innovation. Also, an augmented Kalman filter has been combined with the recursive least-squares method and dynamic programming to identify the forces acting on a dynamic structure (Lourens et al (2012)).…”

Section: Introductionmentioning

confidence: 99%

Identification of external force for slender flexible structures via sparse modelling with an under-relaxation scheme

Nishi,

Song,

Qian

2021

IJMIC

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This study proposes a method for identifying unknown external forces that act on slender flexible bodies. In this method, the external force is represented as an expansion in the Chebyshev polynomials. The expansion coefficients are determined to minimize the number of nonzero coefficients and the difference between the measured and estimated quantities. An under-relaxation scheme is applied to enhance the robustness of computations for the identification. Static and dynamic beam problems are solved using the method to examine how well it can reconstruct the external forces. Numerical computations and analyses of the results demonstrate that the method generally produces a satisfactory external force reconstruction, and its accuracy is sensitive to the choice of measuring device.

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Section: Introductionmentioning

confidence: 99%

Identification of external force for slender flexible structures via sparse modelling with an under-relaxation scheme

Nishi,

Song,

Qian

2021

IJMIC

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“…The Kalman filter is a system dynamic estimation algorithm which produces an estimation of unknown variables using a series of measurements observed over time containing statistical noise and other inaccuracies. This method has been used successfully in the estimation of the critical parameters of the system, such as force [19][20][21][22], structural damage diagnosis [23], inverse heat conduction [24], pore water electrical conductivity [25], and mobile-robot attitude [26] and dynamic state [27][28][29]. Additionally, compared with other algorithms, such as dual Kalman filter [30], join Kalman filter [31], and even recursive least squares (RLS) [32], Kalman filtering is not only easier to achieve for estimating the main parameters in the discrete-time dynamic system, but also can save computing time.…”

Section: Introductionmentioning

confidence: 99%

A Novel Method for the Dynamic Coefficients Identification of Journal Bearings Using Kalman Filter

Kang

Shi

Zhang

et al. 2020

Sensors

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The dynamic coefficients identification of journal bearings is essential for instability analysis of rotation machinery. Aiming at the measured displacement of a single location, an improvement method associated with the Kalman filter is proposed to estimate the bearing dynamic coefficients. Firstly, a finite element model of the flexible rotor-bearing system was established and then modified by the modal test. Secondly, the model-based identification procedure was derived, in which the displacements of the shaft at bearings locations were estimated by the Kalman filter algorithm to identify the dynamic coefficients. Finally, considering the effect of the different process noise covariance, the corresponding numerical simulations were carried out to validate the preliminary accuracy. Furthermore, experimental tests were conducted to confirm the practicality, where the real stiffness and damping were comprehensively identified under the different operating conditions. The results show that the proposed method is not only highly accurate, but also stable under different measured locations. Compared with the conventional method, this study presents a more than high practicality approach to identify dynamic coefficients, including under the resonance condition. With high efficiency, it can be extended to predict the dynamic behaviour of rotor-bearing systems.

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Field Identification of Dynamic Coefficients of Journal Bearings on Flexible Rotor-Bearing System

Kang

Zhang

et al. 2020

Advances in Asset Management and Condition Monitoring

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Input force identification using Kalman filter techniques: application to soil-pile interaction

Cited by 4 publications

References 9 publications

Identification of external force for slender flexible structures via sparse modelling with an under-relaxation scheme

Identification of external force for slender flexible structures via sparse modelling with an under-relaxation scheme

A Novel Method for the Dynamic Coefficients Identification of Journal Bearings Using Kalman Filter

Field Identification of Dynamic Coefficients of Journal Bearings on Flexible Rotor-Bearing System

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