A latent restoring force approach to nonlinear system identification. (15th November 2022)

• Record Type: Journal Article
• Authors: Rogers, T.J.; Friis, T.
• Is Part Of: Mechanical systems and signal processing. Volume 180(2022)
• Publication Date: 2022-11-15
• Subjects: Bayesian -- Nonlinear system identification -- Gaussian process -- Grey-box model; Structural dynamics -- Periodicals; Vibration -- Periodicals; Constructions -- Dynamique -- Périodiques; Vibration -- Périodiques; Structural dynamics; Vibration; Periodicals; 621
• DOI: 10.1016/j.ymssp.2022.109426
• Languages: English
• ISSNs: 0888-3270

Abstract

Abstract: Identification of nonlinear dynamic systems remains a significant challenge across engineering. This work suggests an approach based on Bayesian filtering to extract and identify the contribution of an unknown nonlinear term in the system which can be seen as an alternative viewpoint on restoring force surface type approaches. To achieve this identification, the contribution which is the nonlinear restoring force is modelled, initially, as a Gaussian process in time. That Gaussian process is converted into a state–space model and combined with the linear dynamic component of the system. Then, by inference of the filtering and smoothing distributions, the internal states of the system and the nonlinear restoring force can be extracted. In possession of these states a nonlinear model can be constructed. The approach is demonstrated to be effective in both a simulated case study and on an experimental benchmark dataset. Graphical abstract: Highlights: A novel interpretation of restoring force surface identification is made using Gaussian process latent force models. Unknown nonlinear terms can be recovered by fitting identified "latent restoring forces". Performance is demonstrated on simulated data and experimental benchmark datasets.