Tutorial: Wavelet Analysis and Signal Decomposition for Nonlinear Signals by Keegan Moore, July 2025

Tutorial: Wavelet Analysis and Signal Decomposition for Nonlinear Signals

Keegan Moore

Professor, Georgia Institute of Technology

July 8, 2025

Abstract:

This tutorial addresses the challenges of analyzing complex signals from nonlinear mechanical structures undergoing vibrations. Such signals, whether from measurements or simulations, are often non-stationary and rich in nonlinear features, demanding advanced processing techniques. We will introduce the wavelet transform (WT)and highlight its efficacy in providing time-frequency representations crucial for understanding transient behavior in nonlinear systems. The tutorial will also cover some signal decomposition methodologies including the wavelet-bounded empirical mode decomposition (WBEMD) for adaptive data-driven component separation and the inverse wavelet transform for targeted feature isolation. Furthermore, we will demonstrate how to use WBEMD for data-driven identification and characterization of nonlinear resonances between modes and components. Participants will gain insights into applying these tools to dissect vibrational responses, extract salient features, and better understand the intricate dynamics of nonlinear structures.

Biography:

Keegan J. Moore is an Associate Professor in Aerospace Engineering at the Georgia Institute of Technology where he leads the Moore Dynamics and Analytics Laboratory (MoDAL). He received his Ph.D. from the University of Illinois in 2018 and his B.Sc. from the University of Akron in 2014. He is an expert in nonlinear dynamics and vibrations and his research lies at intersection of theory, simulation, and measurements. His recent work focuses on novel system identification methods, non-reciprocity and energy guiding in nonlinear structures, the mechanics of loosening of bolts, autonomous vibration testing, and autonomous model updating. He is the recipient of the 2022 AFOSR Young Investigator Program Award and a 2023 NSF CAREER Award.