A Gaussian process latent force model for joint input-state estimation in linear structural systems. (1st August 2019)

• Record Type: Journal Article
• Authors: Nayek, Rajdip; Chakraborty, Souvik; Narasimhan, Sriram
• Is Part Of: Mechanical systems and signal processing. Volume 128(2019)
• Page Start: 497
• Page End: 530
• Publication Date: 2019-08-01
• Subjects: Input estimation -- State estimation -- Force identification -- Latent force models -- Gaussian process -- Linear system -- Time-invariant; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2019.03.048
• Languages: English
• ISSNs: 0888-3270

Abstract

Highlights: A novel Gaussian process latent force model for joint input-state estimation in structural dynamics is proposed. The GPLFM is shown as a generalization of the class of input-augmented state-space models. The GPLFM is observable and is proved to be robust against drift encountered in force estimation. The parameters of GPLFM are tuned using maximum likelihood optimization. Performance of GPLFM has been compared with conventional Kalman filter based input estimation algorithms. Abstract: The problem of combined state and input estimation of linear structural systems based on measured responses and a priori knowledge of structural model is considered. A novel methodology using Gaussian process latent force models is proposed to tackle the problem in a stochastic setting. Gaussian process latent force models (GPLFMs) are hybrid models that combine differential equations representing a physical system with data-driven non-parametric Gaussian process models. In this work, the unknown input forces acting on a structure are modelled as Gaussian processes with some chosen covariance functions which are combined with the mechanistic differential equation representing the structure to construct a GPLFM. The GPLFM is then conveniently formulated as an augmented stochastic state-space model with additional states representing the latent force components, and the joint input and state inference of the resulting model is implemented using Kalman filter. The augmented state-space model of GPLFM is shown as a generalization of the class of input-augmented state-space models, is proven observable, and is robust against drift in force estimation compared to conventional augmented formulations. The hyperparameters governing the covariance functions are estimated using maximum likelihood optimization based on the observed data, thus overcoming the need for manual tuning of the hyperparameters by trial-and-error. To assess the performance of the proposed GPLFM method, several cases of state and input estimation are demonstrated using numerical simulations on a 10-dof shear building and a 76-storey ASCE benchmark office tower. Results obtained indicate the superior performance of the proposed approach over conventional Kalman filter based approaches.