Real-time simultaneous input-state-parameter estimation with modulated colored noise excitation

Huang, Ke; Yuen, Ka‐Veng; Wang, Lei

doi:10.1016/j.ymssp.2021.108378

Cited by 18 publications

(12 citation statements)

References 44 publications

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“…With the usage of an online tracking matrix, Zhang et al [43] proposed an EKF-UI approach for real-time structural identifcation. By modelling the unknown inputs as modulated colored noise and using Bayesian methodology to recursively update the noise covariance matrices, Huang et al [44] proposed an EKF-based real-time input-state-parameter estimation approach.…”

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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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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“…Relevant studies have been conducted in the time‐domain and time‐frequency domain. The state space model‐based methods have shown a high efficiency in the time‐domain to track the change of physical parameters 5–15 . Among these methods, the Kalman filter (KF) series methods have been commonly used with an outstanding feature that only incomplete measurements are required in the identification process 6–15 .…”

Section: Introductionmentioning

confidence: 99%

“…The state space model-based methods have shown a high efficiency in the time-domain to track the change of physical parameters. [5][6][7][8][9][10][11][12][13][14][15] Among these methods, the Kalman filter (KF) series methods have been commonly used with an outstanding feature that only incomplete measurements are required in the identification process. [6][7][8][9][10][11][12][13][14][15] Traditional state-parameter estimation techniques such as extended Kalman filter (EKF) or unscented Kalman filter (UKF) were originally proposed for the timeinvariant systems, and they could not be used directly to identify the time-varying parameters.…”

Section: Introductionmentioning

confidence: 99%

“…[5][6][7][8][9][10][11][12][13][14][15] Among these methods, the Kalman filter (KF) series methods have been commonly used with an outstanding feature that only incomplete measurements are required in the identification process. [6][7][8][9][10][11][12][13][14][15] Traditional state-parameter estimation techniques such as extended Kalman filter (EKF) or unscented Kalman filter (UKF) were originally proposed for the timeinvariant systems, and they could not be used directly to identify the time-varying parameters. Adaptive identification techniques based on EKF [7][8][9][10] or UKF 6,11 have the potential to track time-varying parameters by introducing the fadingfactor, adaptive correction factor or adaptive factor matrix to deal with the evolution of system variation.…”

Section: Introductionmentioning

confidence: 99%

“…Most of these methods are conducted for the time-invariant system [12][13][14] ; others are proposed for the identification of time-varying structural parameters. For example, Huang et al 15 conducted a comprehensive review about the joint input-state-parameter estimation algorithms and improved it by introducing the corresponding modeling equation for the unknown inputs into EKF. However, an empirical fading-factor formula is utilized in this algorithm, and further exploration is still needed in the identification of gradually changing physical parameters.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Identification of gradually varying physical parameters based on discrete cosine transform using partial measurements

Yang

Lei

et al. 2022

Structural Contr & Hlth

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Structural physical parameters often vary gradually due to the degradation of material properties or effects of environment. In this paper, two novel approaches are proposed to identify the gradually varying physical parameters based on the discrete cosine transform (DCT) using partial measurements of structural responses. Approach I is proposed for the circumstance of known excitations. The gradually varying physical parameters are first located by the fading-factor extended Kalman filter (FEKF) and then identified by the proposed DCT integrated with Kalman filter (KF) method. Approach II is proposed for the identification of gradually varying physical parameters under unknown excitations. The gradually varying physical parameters are first localized by the proposed fading-factor extended Kalman filter under unknown input (FEKF-UI) and then identified by the proposed DCT integrated with Kalman filter under unknown input (KF-UI). Numerical examples demonstrate that the proposed approaches can identify the gradually varying physical parameters accurately with incomplete measurement data. Moreover, the identification of time-varying cable force in cable-stayed bridge is also discussed as a case study of the proposed approach I. Experimental verification shows that it provides a new path to identify the time-varying cable force by only using one acceleration response measurement of the cable.

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Synchronous identification of nonlinear structural parameters and unknown external excitation based on improved UKF

Yuan,

Wang,

Xin

et al. 2024

Engineering Structures

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Real-time simultaneous input-state-parameter estimation with modulated colored noise excitation

Cited by 18 publications

References 44 publications

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

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

Identification of gradually varying physical parameters based on discrete cosine transform using partial measurements

Synchronous identification of nonlinear structural parameters and unknown external excitation based on improved UKF

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