On the application of Gaussian process latent force models for joint input-state-parameter estimation: With a view to Bayesian operational identification

• Published in: Mechanical systems and signal processing Vol. 140; p. 106580
• Main Authors: Rogers, T.J., Worden, K., Cross, E.J.
• Format: Journal Article
• Language: English
• Published: Berlin Elsevier Ltd 01.06.2020; Elsevier BV
• Subjects: Bayesian; Bayesian analysis; Gaussian process; Identification; Latent force model; Operational modal analysis; Parameter estimation; Parameter identification; Random noise; Signal processing; State space models; Statistical inference; Structural health monitoring; System identification; Gaussian process; System identification; Bayesian; Operational modal analysis; Latent force model

•An output only analysis of structural systems under non-Gaussian excitation is shown.•The GPLFM is shown for joint input-state-parameter identification for wave loading.•MCMC solutions of the model recover posterior distributions of parameters in the system.•The extension of the model for use in mo...