Fabro, J.
, Vogl, G.
, Qu, Y.
and Eischens, R.
(2022),
Learning Data-Driven Model of Damped Coupled Oscillators from System Impulse Response, Proceedings of the Annual Conference of the Prognostics and Health Management Society 2022, Nashville, TN, US, [online], https://doi.org/10.36001/phmconf.2022.v14i1.3204, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=935029
(Accessed April 19, 2024)
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
Secure .gov websites use HTTPS
A lock (
) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.
Learning Data-Driven Model of Damped Coupled Oscillators from System Impulse Response
Published
Author(s)
Jacob Fabro, , Yongzhi Qu, Reese Eischens
Abstract
Data-driven modeling of dynamical systems has drawn much research attention recently, with the goal of approximating the underlying governing rules of a dynamical system with data-driven differential equations. Many real-world dynamical systems can be modeled as coupled oscillators, such as chemical reactors, ecological systems, integrated circuits, and mechanical systems. In those systems, the response can be described by coupled differential equations in which multi-dimensional state variables describe the timeevolution. However, in practice, the full set of state variables is often difficult or expensive to measure. This paper shows an attempt to develop a data-driven model for damped coupled oscillators from a mixed-mode response signal using neural differential equations. The univariate time-series data of the impulse response is first resampled into a multi-variate time-delayed embedding. A singular value decomposition is then applied to find the dominant orthogonal basis (oscillator modes). The decoupled modes are then modeled with parameterized neural differential equations. The unknown parameters can be learned from a segment of historical data. The proposed methodology is validated using impact testing data of an end mill in a machine tool spindle. The results demonstrate that the proposed method can effectively model damped coupled oscillators.Proceedings Title
Proceedings of the Annual Conference of the Prognostics and Health Management Society 2022
Volume
41
Issue
1
Conference Dates
November 1-4, 2022
Conference Location
Nashville, TN, US
Conference Title
14th Annual Conference of the Prognostics and Health Management Society
Pub Type
Conferences
Download Paper
Citation
Created October 28, 2022, Updated December 7, 2022