Dynamic State Analysis of a Driven Magnetic Pendulum using Ordinal Partition Networks and Topological Data Analysis

TL;DR

This paper introduces a novel method combining ordinal partition networks and topological data analysis to detect state changes in a driven magnetic pendulum, effectively distinguishing between periodic and chaotic behaviors.

Contribution

The work applies a network-topology analysis pipeline to non-autonomous nonlinear systems, enabling automatic detection of dynamic state transitions from time series data.

Findings

Successfully distinguishes periodic and chaotic states

First application of network-TDA pipeline to non-autonomous systems

Potential for automatic design and fault detection tools

Abstract

The use of complex networks for time series analysis has recently shown to be useful as a tool for detecting dynamic state changes for a wide variety of applications. In this work, we implement the commonly used ordinal partition network to transform a time series into a network for detecting these state changes for the simple magnetic pendulum. The time series that we used are obtained experimentally from a base-excited magnetic pendulum apparatus, and numerically from the corresponding governing equations.The magnetic pendulum provides a relatively simple, non-linear example demonstrating transitions from periodic to chaotic motion with the variation of system parameters. For our method, we implement persistent homology, a shape measuring tool from Topological Data Analysis (TDA), to summarize the shape of the resulting ordinal partition networks as a tool for detecting state changes. We show that this network analysis tool provides a clear distinction between periodic and chaotic time series. Another contribution of this work is the successful application of the networks-TDA pipeline, for the first time, to signals from non-autonomous nonlinear systems. This opens the door for our approach to be used as an automatic design tool for studying the effect of design parameters on the resulting system response. Other uses of this approach include fault detection from sensor signals in a wide variety of engineering operations.