Improved force identification with augmented Kalman filter based on the sparse constraint

Wei, Da; Li, Dongsheng; Huang, Jinhai

doi:10.1016/j.ymssp.2021.108561

Cited by 22 publications

(13 citation statements)

References 28 publications

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“…In this figure, the true force value is indicated by the gray solid line, the estimated result based on DUKF is indicated by the red dashed line, the result using the UMUKF is indicated by the blue dot line, the estimated result based on the PM1-UMUKF is indicated by the green dot dash line, and the estimated result based on the PM2-UMUKF is indicated by the purple double dot dash line. In PM1-UMUKF and PM2-UMUKF, sparse constraints on the force vectors are required, and to avoid under-determination problems [47], the number of loads to be identified should be less than or equal to the number of sensors. In addition, to improve the accuracy of load identification, the number of loads to be identified should be as small as possible.…”

Section: Validation Of the Proposed Pm2-umukfmentioning

confidence: 99%

Structural damage identification with unknown external inputs based on the sparse constraint

Wei

Cai

et al. 2023

Smart Mater. Struct.

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The strength and integrity of a structure depends not only on the quality of its materials but also the health condition of its different components. In some cases, damage cracks inside the structural will be generated from excessive loads, and if these damage cracks go undetected for a long period, catastrophic failure of structures may occur. However, most of the damages at early stages are minor and difficult to detect by visual inspection. Therefore, this paper proposes a damage estimation algorithm based on unscented Kalman filter (UKF), which can identify and locate the damage parameters in real time with a limited number of sensors. Meanwhile, this algorithm is also applicable to the structural system with unknown external inputs, and can make the joint force-damage estimation. For most structures, the damage parameters are sparse in space, therefore, the sparsity of the damage parameter vector is introduced to UKF as an l1-norm constraint by the pseudo- measurement (PM) technique. Thus, unconstrained optimization of the damage parameter estimation is transformed into an l1-norm constrained optimization problem. With such improvement, the process of damage parameter estimation converges faster and the false damage parameters are effectively restrained. Moreover, in order to solve the force drift problem during force identification when only acceleration data is used, the sparse constraint of the force vector is also introduced to UKF framework by the PM technique. Finally, numerical simulations of a ten-story shear building and experiments of a three-story shear structure are used to demonstrate the performance of the proposed algorithm. The results indicate that this algorithm can achieve an accurate estimation of damage and can successfully resolve the common force drift problem.

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Section: Validation Of the Proposed Pm2-umukfmentioning

confidence: 99%

Structural damage identification with unknown external inputs based on the sparse constraint

Wei

Cai

et al. 2023

Smart Mater. Struct.

Self Cite

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“…Accordingly, E f and E y are used as extraction matrices to extract the location of the load and the responses respectively. When the number of measured points is k and the number of concentrated loads is l, the dynamic equation can be substituted as Equation (15).…”

Section: [R (𝜂mentioning

confidence: 99%

“…Ding et al 14 proposed a precise integration method for the nonlinear dynamic analysis of structures. Wei et al 15 established a new augmented state‐space model based on the augmented exact integration method. Wang et al 16 derived a time‐domain load identification method based on a decoupled second‐order formula for nonproportional damping systems with precise integration.…”

Section: Introductionmentioning

confidence: 99%

Multisource uncertain dynamic load identification fitted by Legendre polynomial based on precise integration and the Savitzky–Golay filters

Wang

et al. 2022

Numerical Meth Engineering

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In this article, a precise integral method (PIM) for the load identification of continuous structures based on the polynomial nonlinear hypothesis is presented.Essentially, load identification can be regarded as solving a dynamic equation in an inverse process. PIM is famous for its high precision in positive engineering problems. It has been already used in load reconstruction, but most cases are with discrete structures. In each time step, the dynamic responses of the measured points are used to identify the load without establishing a recursive chain, so PIM is not sensitive to the initial value and has no accumulated error. Legendrepolynomials (LPIM) is a computational scheme. The dynamic load is assumed to be a polynomial fitting nonlinear function in each integral time step with LPIM. Just with the first modal, LPIM can reliably identify the concentrated load. In addition, as uncertainty is getting more and more attention in engineer, it is also needed to be considered in load identification. In linear systems, interval vertex is a reliable method to obtain the load bounds in the virtue of multisource uncertainties. The results of identified load are too lumpy because of the polynomial fitting. To improve the precision, Savitsky-Golay (S-G) filters which fit the curves by polynomials in a frame length is introduced to smooth down the load in high fidelity. Eight numerical examples are investigated to demonstrated the efficiency and precision of the developed method.

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“…Using modal expansion, Eftekhar Azam et al [36] proposed an AKF-based approach in the modal domain for input-state estimation of steel railway bridges. Based on sparse constraint theory, Wei et al [37] proposed an AKF approach for improved force identifcation.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Identification of Time-Varying Parameters and External Loads Based on Extended Kalman Filter: Approach and Validation

Zhang

Hua

et al. 2023

Structural Control and Health Monitoring

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Online identification of time-variant parameters without knowledge of external loads is an important but challenging task for structural health monitoring and vibration control. In this study, a two-stage approach, named extended Kalman filter with forgetting factor matrix under unknown inputs (EKF-FFM-UI), is proposed for simultaneously identifying the time-variant parameters and external loads. In stage 1, an extended Kalman filter under unknown inputs (EKF-UI) approach previously proposed by the authors is employed for estimating the structural states and unknown loads. This EKF-UI approach is solely suitable for time-invariant system identification. Therefore, the aim of stage 2 is to improve this approach for the purpose of possessing tracking capability. In this stage, the acceleration responses are first reconstructed by using the differential equation of motion and employed for improving the accuracy of estimated structural states. A forgetting factor matrix is introduced into the priori estimation error covariance matrix to track time-varying parameters. The square errors between the measurements and the corresponding estimates are defined as an index and used for detecting the damage time instant. Then, a covariance resetting technique is employed to assure that such changes in structural parameters can be efficiently captured. A shear-type building structure without/with magneto-rheological (MR) dampers and a fixed beam structure are used as numerical examples for validating the effectiveness of the proposed approach. Experimental tests on a six-story building model are also conducted. Results show the time-varying parameters and unknown inputs can be simultaneously identified with acceptable accuracy.

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Improved force identification with augmented Kalman filter based on the sparse constraint

Cited by 22 publications

References 28 publications

Structural damage identification with unknown external inputs based on the sparse constraint

Structural damage identification with unknown external inputs based on the sparse constraint

Multisource uncertain dynamic load identification fitted by Legendre polynomial based on precise integration and the Savitzky–Golay filters

Simultaneous Identification of Time-Varying Parameters and External Loads Based on Extended Kalman Filter: Approach and Validation

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