A Bayesian smoothing for input‐state estimation of structural systems

2021 ◽  
Author(s):  
Mohsen Ebrahimzadeh Hassanabadi ◽  
Amin Heidarpour ◽  
Saeed Eftekhar Azam ◽  
Mehrdad Arashpour
Keyword(s):  
State Estimation ◽  
Structural Systems ◽  
Input State ◽  
Bayesian Smoothing

Input-state-parameter estimation of structural systems from limited output information

2019 ◽  
Vol 126 ◽  
pp. 711-746 ◽  
Author(s):  
V.K. Dertimanis ◽  
E.N. Chatzi ◽  
S. Eftekhar Azam ◽  
C. Papadimitriou
Keyword(s):  
Parameter Estimation ◽  
State Parameter ◽  
Structural Systems ◽  
Input State ◽  
Limited Output ◽  
Output Information

scholarly journals Bayesian Joint Input-State Estimation for Nonlinear Systems

Vibration ◽  
2020 ◽  
Vol 3 (3) ◽  
pp. 281-303
Author(s):  
Timothy J. Rogers ◽  
Keith Worden ◽  
Elizabeth J. Cross
Keyword(s):  
Nonlinear Systems ◽  
Gaussian Process ◽  
State Estimation ◽  
State Space ◽  
Mean Squared Error ◽  
Duffing Oscillator ◽  
State Space Model ◽  
Input State ◽  
Force Model ◽  
Internal States

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.

scholarly journals Verification of joint input-state estimation for force identification by means of in situ measurements on a footbridge

2016 ◽  
Vol 75 ◽  
pp. 245-260 ◽  
Author(s):  
K. Maes ◽  
K. Van Nimmen ◽  
E. Lourens ◽  
A. Rezayat ◽  
P. Guillaume ◽  
...  
Keyword(s):  
State Estimation ◽  
In Situ Measurements ◽  
Input State ◽  
Force Identification
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