Kalman filter-based force estimation in a clamped plate using reduced order model and noisy measurements

Shrivastava, Akash; Mohanty, Amiya Ranjan

doi:10.1080/17415977.2018.1503657

Cited by 4 publications

(2 citation statements)

References 29 publications

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“…Table 1 describes the equations in these two steps. Additionally, the extensive derivation of these parameters can be found in [12]. Yet, the aforementioned formulations are unable to estimate the response of the system with an unknown input.…”

Section: Mathematical Formulationmentioning

confidence: 99%

Assessment of a Dual Kalman Filter-Based Approach for Input/Output Estimation in an Aluminum Plate

Sattarifar

Nestorović

2021

Lecture Notes in Civil Engineering

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Vulnerability of structures to damage during their service time brings up the necessity of design and implementation of an intelligent procedure to assure the health of the structure. In the sight of this requisite, current work deals with extending the capability of a dual Kalman filter (DKF) state estimation scheme to assist vibration-based health monitoring methods. This is met by estimating the response of the structure for locations at which a sensor cannot be placed. The capability of the DKF method in the estimation of states of a linear system with an unknown input has been presented in various recent works. In this paper, a DKF approach incorporated with a reduced order structural model (in this case an aluminum plate) is utilized to obtain an estimation of applied force and the response of the structure in terms of acceleration, velocity, and displacement. These estimations are based on measured accelerations at a limited number of points on the aluminum plate as well as the state-space model of the dynamic system. Numerical simulations and experimental works are performed to obtain the mentioned datasets. To assess the robustness of the method concerning various conditions, the effect of the frequency, as well as type of the function of the input force on the validity of the method, is presented. Moreover, it is shown to what extent the number of selected modes in model reduction procedure can influence the accuracy of the DKF technique.

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Section: Mathematical Formulationmentioning

confidence: 99%

Assessment of a Dual Kalman Filter-Based Approach for Input/Output Estimation in an Aluminum Plate

Sattarifar

Nestorović

2021

Lecture Notes in Civil Engineering

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“…Moving force identification (MFI) is a special force reconstruction problem, which has been widely studied in the past decades [4][5][6]. Lots of MFI methods have been proposed [7][8][9] and they can be roughly divided into four categories, including direct methods, regularization-based methods, swarm intelligence-based (SI-based) methods and other MFI methods.…”

Section: Introductionmentioning

confidence: 99%

A sparse self-estimated sensor-network for reconstructing moving vehicle forces

Pan¹,

Liu²,

Yu³

2019

Smart Mater. Struct.

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Reconstructing moving vehicle forces from structural responses is an important inverse problem in bridge engineering. When a sensor-network is applied to moving force identification (MFI), a necessary task is to specify the contribution of different sensors at the first beginning. This paper proposes a novel method based on the sparse self-estimated sensor-network for estimating moving vehicle forces on bridges. Firstly, an over-completed dictionary is pre-defined to ensure a sparse representation of moving vehicle forces. Images corresponding to force atoms, i.e. structural responses caused by unit force components, are used to match the structural responses. As a self-estimated sensor-network, the signal features, noise energy and signal-to-noise ratio of each sensor can be self-estimated by combining sparse regularization and Bayesian information criterion. Then an improved L 2 -norm regularization model, in which the cost function is defined by the weighted residuals of sensors, is proposed and applied to solve the MFI problem. Finally, both numerical and experimental examples are conducted to assess the accuracy and the feasibility of the proposed method. Illustrated results clearly show the robustness and applicability of the proposed method. Some related issues are discussed as well.

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Force Estimation on a Clamped Plate Using a Deterministic–Stochastic Approach

Shrivastava

Mohanty

2020

Recent Advances in Theoretical, Applied, Computational and Experimental Mechanics

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Kalman filter-based force estimation in a clamped plate using reduced order model and noisy measurements

Cited by 4 publications

References 29 publications

Assessment of a Dual Kalman Filter-Based Approach for Input/Output Estimation in an Aluminum Plate

Assessment of a Dual Kalman Filter-Based Approach for Input/Output Estimation in an Aluminum Plate

A sparse self-estimated sensor-network for reconstructing moving vehicle forces

Force Estimation on a Clamped Plate Using a Deterministic–Stochastic Approach

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