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

Song, Xuegang; Zhang, Yuexin; Liang, Dakai

doi:10.3390/s17081733

Cited by 8 publications

(9 citation statements)

References 14 publications

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“…Dui et al [2] discussed three key issues affecting load identification based on strain measurement, but the selection of the number and location of strain measuring points that affect the accuracy of load identification has not been studied. Song Xuegang et al [3] used eight optical fiber sensors arranged at the midline of the cantilever beam to identify the unidirectional load acting on the cantilever beam. Wu Xiao et al [4] used six optical fiber sensors arranged at the mid-axis of a variable-section cantilever beam to identify the unidirectional load with known action points.…”

Section: Introductionmentioning

confidence: 99%

Structural load identification based on strain response and experimental validation

Xu,

Luo,

Qin

et al. 2023

Ninth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2023)

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Accurate identification of external loads on structures is of great significance for structural health monitoring and intelligent operation and maintenance. Aiming at the problem of load identification with known position and unknown magnitude, a structural load identification method based on strain response is proposed. Firstly, the load coefficient matrix between the strain of the measuring point and the unit load is established by numerical simulation; Then the optimal arrangement of strain measuring points is obtained based on the D-optimal design principle, and strain gauges are pasted to the structure to collect strain information under actual working conditions; Finally, the load magnitude acting on the structure is calculated based on the load coefficient matrix and the measured strain. Taking the variable-section cantilever beam structure as an example, a unidirectional excitation is applied to its free end to cause bending deformation, and the strain response at two positions is used to identify the load acting on its free end. The experimental results demonstrate the effectiveness of the proposed method.

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Section: Introductionmentioning

confidence: 99%

Structural load identification based on strain response and experimental validation

Xu,

Luo,

Qin

et al. 2023

Ninth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2023)

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“…To further improve the identification accuracy, in the follow-up studies, some other regularization methods [ 18 ], such as the function expansion method [ 20 , 21 , 22 ], multiplicative regularization [ 23 , 24 ], sparse regularization [ 25 , 26 , 27 , 28 ], and so on [ 29 , 30 , 31 , 32 ], are introduced into the force identification problem. In particular, for the time-varying external force, it is usually not sparse in the time domain.…”

Section: Introductionmentioning

confidence: 99%

Identification of Time-Varying External Force Using Group Sparse Regularization and Redundant Dictionary

Liu

2022

Sensors

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How to accurately identify unknown time-varying external force from measured structural responses is an important engineering problem, which is critical for assessing the safety condition of the structure. In the context of a few available accelerometers, this paper proposes a novel time-varying external force identification method using group sparse regularization based on the prior knowledge in the redundant dictionary. Firstly, the relationship between time-varying external force and acceleration responses is established, and a redundant dictionary is designed to create a sparse expression of external force. Then, the relevance of atoms in the redundant dictionary is revealed, and this prior knowledge is used to determine the group structures of atoms. As a result, a force identification governing equation is formulated, and the group sparse regularization is reasonably introduced to ensure the accuracy of the identified results. The contribution of this paper is that the group structures of atoms are reasonably determined based on prior knowledge, and the complexity in the process for identifying external force from measured acceleration responses is reduced. Finally, the effectiveness of the proposed method is demonstrated by numerical simulations and an experimental structure. The illustrated results show that, compared with the force identification method based on the standard l1-norm regularization, the proposed method can further improve the identified accuracy of unknown external force and greatly enhance the computational efficiency for the force identification problem.

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

Cited by 8 publications

References 14 publications

Structural load identification based on strain response and experimental validation

Structural load identification based on strain response and experimental validation

Identification of Time-Varying External Force Using Group Sparse Regularization and Redundant Dictionary

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

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