Deep learning architectures for data-driven damage detection in nonlinear dynamic systems

TL;DR

This paper explores deep learning models, specifically autoencoders and GANs, for unsupervised damage detection in nonlinear dynamic systems using vibration data, validated through numerical and experimental studies.

Contribution

It introduces a novel unsupervised deep learning approach employing AEs and GANs with 1D CNNs for damage detection without prior system knowledge.

Findings

Effective damage detection in nonlinear systems demonstrated

Unsupervised approach works with varying excitation intensities

Experimental validation confirms numerical results

Abstract

The primary goal of structural health monitoring is to detect damage at its onset before it reaches a critical level. The in-depth investigation in the present work addresses deep learning applied to data-driven damage detection in nonlinear dynamic systems. In particular, autoencoders (AEs) and generative adversarial networks (GANs) are implemented leveraging on 1D convolutional neural networks. The onset of damage is detected in the investigated nonlinear dynamic systems by exciting random vibrations of varying intensity, without prior knowledge of the system or the excitation and in unsupervised manner. The comprehensive numerical study is conducted on dynamic systems exhibiting different types of nonlinear behavior. An experimental application related to a magneto-elastic nonlinear system is also presented to corroborate the conclusions.