Identification of time-varying systems with partial acceleration measurements by synthesis of wavelet decomposition and Kalman filter

Chen, Siyi; Lu, Jubin; Lei, Ying

doi:10.1177/1687814020930460

Cited by 7 publications

(3 citation statements)

References 27 publications

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“…Currently, there are two main methods for identifying TMPs. One type is the segmented analysis method [ 29 ], which divides response data into small time segments, treating the modal parameters as time-invariant within each segment. The modal parameters identified in each segment are then fitted with a curve to obtain the time-varying patterns.…”

Section: Introductionmentioning

confidence: 99%

Modal Parameter Recursive Estimation of Concrete Arch Dams under Seismic Loading Using an Adaptive Recursive Subspace Method

Zhu,

Qiu,

et al. 2024

Sensors

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Modal parameter estimation is crucial in vibration-based damage detection and deserves increased attention and investigation. Concrete arch dams are prone to damage during severe seismic events, leading to alterations in their structural dynamic characteristics and modal parameters, which exhibit specific time-varying properties. This highlights the significance of investigating the evolution of their modal parameters and ensuring their accurate identification. To effectively accomplish the recursive estimation of modal parameters for arch dams, an adaptive recursive subspace (ARS) method with variable forgetting factors was proposed in this study. In the ARS method, the variable forgetting factors were adaptively updated by assessing the change rate of the spatial Euclidean distance of adjacent modal frequency identification values. A numerical simulation of a concrete arch dam under seismic loading was conducted by using ABAQUS software, in which a concrete damaged plasticity (CDP) model was used to simulate the dam body’s constitutive relation, allowing for the assessment of damage development under seismic loading. Utilizing the dynamic responses obtained from the numerical simulation, the ARS method was implemented for the modal parameter recursive estimation of the arch dam. The identification results revealed a decreasing trend in the frequencies of the four initial modes of the arch dam: from an undamaged state characterized by frequencies of 0.910, 1.166, 1.871, and 2.161 Hz to values of 0.895, 1.134, 1.842, and 2.134 Hz, respectively. Concurrently, increases in the damping ratios of these modes were observed, transitioning from 4.44%, 4.28%, 5.42%, and 5.56% to 4.98%, 4.91%, 6.61%, and 6.85%%, respectively. The correlation of the identification results with damage progression validated the effectiveness of the ARS method. This study’s outcomes have substantial theoretical and practical importance, facilitating the immediate comprehension of the dynamic characteristics and operational states of concrete arch dam structures.

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Section: Introductionmentioning

confidence: 99%

Modal Parameter Recursive Estimation of Concrete Arch Dams under Seismic Loading Using an Adaptive Recursive Subspace Method

Zhu,

Qiu,

et al. 2024

Sensors

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“…By using an innovation error and the corresponding trigger parameter, Schleiter and Altay [21] proposed a UKF-based approach for the identifcation of abrupt stifness changes in controlled structures. With the combination of KF and wavelet decomposition, Chen et al [22] proposed an approach for identifying time-variant parameters. Based on the concept of pre-estimation of system parameters, Ciolek et al [23] proposed a decoupled KF approach for the estimation of time-varying systems.…”

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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“…Li et al [27] proposed an approach to identify the parameters by combining a time-varying forgetting factor stochastic gradient and KF algorithm. Based on the synthesis of wavelet multiresolution decomposition and KF, Chen et al [28] proposed an approach for the identification of time-varying structural stiffness and damping parameters. This approach was then extended for dealing with the case of unknown inputs [29,30].…”

Section: Introductionmentioning

confidence: 99%

Online Identification of Time-Variant Structural Parameters Under Unknown Inputs Basing On Extended Kalman Filter

Zhang

Hua

et al. 2021

Preprint

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To date, a number of parameter identification methods have been developed for the purpose of structural health monitoring and vibration control. Among them, the extended Kalman filter (EKF) series methods are attractive in view of the efficient unbiased estimation in recursive manner. However, most of these methods are performed on the premise that the parameters are time-invariant and/or the loadings are known. To circumvent the aforementioned limitations, an online EKF with unknown input (OEKF-UI) approach is proposed in this paper for the identification of time-varying parameters and the unknown excitation. A revised observation equation is obtained with the aid of projection matrix. To capture the changes of structural parameters in real-time, an online tracking matrix (OTM) associated with the time-varying parameters is introduced and determined via an optimization procedure. Then, based on the principle of EKF, the recursive solution of structural states including the time-variant parameters can be analytically derived. Finally, using the estimated structural states, the unknown inputs are identified by means of least-squares estimation (LSE) at the same time-step. The effectiveness of the proposed approach is validated via linear and nonlinear numerical examples with the consideration of parameters being varied abruptly.

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Identification of time-varying systems with partial acceleration measurements by synthesis of wavelet decomposition and Kalman filter

Cited by 7 publications

References 27 publications

Modal Parameter Recursive Estimation of Concrete Arch Dams under Seismic Loading Using an Adaptive Recursive Subspace Method

Modal Parameter Recursive Estimation of Concrete Arch Dams under Seismic Loading Using an Adaptive Recursive Subspace Method

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

Online Identification of Time-Variant Structural Parameters Under Unknown Inputs Basing On Extended Kalman Filter

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