An offline approach for output-only Bayesian identification of stochastic nonlinear systems using unscented Kalman filtering

Erazo, Kalil; Nagarajaiah, Satish

doi:10.1016/j.jsv.2017.03.001

Cited by 67 publications

(42 citation statements)

References 33 publications

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“…Despite this difficulty, attention is turning to nonlinear systems; Lei et al [11] present an approach based on an unscented Kalman filter for the task of input-state estimation. Similarly, Erazo and Nagarajaiah [12] consider the use of an unscented Kalman filter for recovery of a nonlinear system and its inputs showing very promising results in an experimental validation case study. Yuen and Beck [13] consider a Bayesian spectral density approach to identify nonlinear systems from output-only measurments, concerned with estimating the parameters of the nonlinear models.…”

Section: Introductionmentioning

confidence: 99%

Bayesian Joint Input-State Estimation for Nonlinear Systems

2020

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This work suggests a solution for joint input-state estimation for nonlinear systems. The task is to recover the internal states of a nonlinear oscillator, the displacement and velocity of the system, and the unmeasured external forces applied. To do this, a Gaussian process latent force model is developed for nonlinear systems. The model places a Gaussian process prior over the unknown input forces for the system, converts this into a state-space form and then augments the nonlinear system with these additional hidden states. To perform inference over this nonlinear state-space model a particle Gibbs approach is used combining a “Particle Gibbs with Ancestor Sampling” Markov kernel for the states and a Metropolis-Hastings update for the hyperparameters of the Gaussian process. This approach is shown to be effective in a numerical case study on a Duffing oscillator where the internal states and the unknown forcing are recovered, each with a normalised mean-squared error less than 0.5%. It is also shown how this Bayesian approach allows uncertainty quantification of the estimates of the states and inputs which can be invaluable in further engineering analyses.

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Section: Introductionmentioning

confidence: 99%

Bayesian Joint Input-State Estimation for Nonlinear Systems

2020

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“…In the ambient test, it is assumed that the unknown excitation is stochastic stationary . Based on this assumption, many methods have been developed for linear and nonlinear systems . Since the excitation is random, there must be some uncertainty existing when identifying modal parameters.…”

Section: Introductionmentioning

confidence: 99%

Vibration properties monitoring and uncertainty quantification of precast and cast‐in‐situ shear wall buildings using a Bayesian framework

Yun²,

Zhang

et al. 2020

Struct Control Health Monit

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Summary Precast buildings have been becoming popular in China in these 5 years since they have many merits such as effectiveness, safety, high quality, and environmental friendliness. In some cities of China, it is required to make the proportion of precast buildings account for 30% in newly built construction areas. Following the rapid development, their vibration behaviors are attracting more attention since it may be quite different from the cast‐in‐situ building. To investigate the difference, this paper studies the modal properties of two kinds of buildings, i.e., new‐type precast building and cast‐in‐situ building. They were built in the same area, while the structural forms are also similar. The main difference is the construction manner. Ambient vibration tests of these two structures were conducted respectively. Since the number of sensors is limited, the locations of interest were measured by a multiple‐setup technique. The difficulty encountered during the field test was discussed. A frequency domain Bayesian approach was used to conduct operational modal analysis. Both the most probable value and posterior uncertainties of modal parameters were studied, including translational, torsional, and wrapping modes. The difference of the modal parameters of the two kinds of buildings are compared and discussed. Finite element models were also built to perform a comparison. It is seen that although the structural forms and appearances of the two buildings are similar, some obvious differences were observed regarding the dynamic characteristics, which provide valuable information for popularizing and future assessment of the new‐type precast structure.

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“…For more in‐depth damage diagnosis, these macroscale dynamical features can be further coupled with computational models through successful implementation of finite element model (FEM) updating schemes when design information about the structure is available (e.g., materials, topology, and connection types) . Considering the required modeling assumptions and the inherent measurement noise, the Bayesian model updating framework offers a robust and rigorous basis for structural condition assessment and consequent reliability evaluation . Essentially, it specifies how to characterize and quantify the uncertainty of the models as well as the predictions against the measurements .…”

Section: Introductionmentioning

confidence: 99%

Structural damage detection using Bayesian inference and seismic interferometry

Uzun

Sun

Smit

et al. 2019

Struct Control Health Monit

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Funding information Shell SummaryWe present a computational methodology for structural identification and damage detection via linking the concepts of seismic interferometry and Bayesian inference. A deconvolution-based seismic interferometry approach is employed to obtain the waveforms that represent the impulse response functions with respect to a reference excitation source. Using the deconvolved waveforms, we study the following two different damage detection methods that utilize shear wave velocity variations: the arrival picking method and the stretching method.We show that variations in the shear wave velocities can be used for qualitative damage detection and that velocity reduction is more evident for more severely damaged states. Second, a hierarchical Bayesian inference framework is used to update a finite element model by minimizing the gap between the predicted and the extracted time histories of the impulse response functions.Through comparison of the model parameter distributions of the damaged structure with the updated baseline model, we demonstrate that damage localization and quantification are possible. The performance of the proposed approach is verified through two shake table test structures. Results indicate that the proposed framework is promising for monitoring structural systems, which allows for noninvasive determination of structural parameters. KEYWORDSBayesian inference, damage detection, probabilistic model updating, seismic interferometry, stretching method, structural health monitoring Struct Control Health Monit. 2019;26:e2445. wileyonlinelibrary.com/journal/stc

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An offline approach for output-only Bayesian identification of stochastic nonlinear systems using unscented Kalman filtering

Cited by 67 publications

References 33 publications

Bayesian Joint Input-State Estimation for Nonlinear Systems

Bayesian Joint Input-State Estimation for Nonlinear Systems

Vibration properties monitoring and uncertainty quantification of precast and cast‐in‐situ shear wall buildings using a Bayesian framework

Structural damage detection using Bayesian inference and seismic interferometry

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