A Modal-Based Kalman Filter Approach and OSP Method for Structural Response Reconstruction

The objective of the work is experimental validation and optimal experimental design for structural response reconstruction. A modal-based Kalman filter approach based on excitation identification Kalman filter is proposed for response reconstruction and excitation estimation of structures by using noisy acceleration and strain measurements. Firstly, different filters are introduced and discussed. Secondly, to avoid single type sensors, a displacement reconstruction based on modal method is introduced into the proposed approach. Thirdly, the backward sequential algorithm is given to obtain the optimal sensor locations. It is shown that the proposed method can avoid the divergency in the estimated process of excitation and displacements as a result of incomplete measurements. Reasonable estimates of strains, displacements, velocities, accelerations, and excitations of structures can be accomplished with few acceleration sensors and strain gauges.

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Experimental validation of the proposed extended Kalman filter with unknown inputs algorithm based on data fusion

Journal of Low Frequency Noise Vibration and Active Control ◽

10.1177/1461348419868860 ◽

2019 ◽

Vol 39 (4) ◽

pp. 835-849 ◽

Cited By ~ 1

Author(s):

Jinshan Huang ◽

Xianzhi Li ◽

Xiongjun Yang ◽

Zhupeng Zheng ◽

Ying Lei

Keyword(s):

Kalman Filter ◽

Data Fusion ◽

Extended Kalman Filter ◽

Experimental Validation ◽

Structural Parameters ◽

Wide Band ◽

Unknown Inputs ◽

Filter Methods ◽

Filter Approach ◽

Shear Building

The extended Kalman filter is a useful tool in the research of structural health monitoring and vibration control. However, the traditional extended Kalman filter approach is only applicable when the information of external inputs to structures is available. In recent years, some improved extended Kalman filter methods applied with unknown inputs have been proposed. The authors have proposed an extended Kalman filter with unknown inputs based on data fusion of partially measured displacement and acceleration responses. Compared with previous approaches, the drifts in the estimated structural displacements and unknown external inputs can be avoided. The feasibility of proposed extended Kalman filter with unknown inputs has been demonstrated by some numerical simulation examples. However, experimental validation of the proposed extended Kalman filter with unknown inputs has not been conducted. In this paper, an experiment is conducted to validate the effectiveness of the proposed approach. A five-story shear building model subjected to an unknown external excitation of wide-band white noise is conducted. Moreover, the data fusion of partially measured strain and acceleration responses from the building is adopted as it is difficult to accurately measure structural displacement in practice. Identified results show that the recently proposed extended Kalman filter with unknown inputs can be applied to identify structural parameters, structural states, and the unknown inputs in real time.

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