Damage identification in structures using dynamic mode decomposition for vibration analysis of low-resolution videos

Abstract

Images are widely employed to analyze vibration signals and extract information for damage detection in noninvasive structural health monitoring (SHM) techniques. However, video quality must often be high in resolution to accurately extract patterns associated with damage in classification processes. In this context, this article introduces a robust approach that enables classifying a structure’s health state using noisy, low-resolution images, which are much cheaper and easier to obtain. The approach involves decomposing these videos using optDMD—dynamic mode decomposition to extract patterns and assess changes over time, proposing a metric for SHM based on video data that detects and localizes damage while providing qualitative information on its extent. To illustrate the formulation, numerical tests on an Euler–Bernoulli beam and experimental validation on a beam with damage caused by varying crack sizes are conducted. Videos of different quality and resolution are used to demonstrate the extraction of both modal characteristics and the contributions of the identified modes to image reconstruction and the detection of the beam’s structural states.