Exploiting time-frequency analysis for damage detection using Generative Adversarial Networks

Structural Health Monitoring (SHM) is of utmost importance to ensure the safety and to enhance the safety and longevity of various engineering structures, from buildings and bridges to aircraft. Detecting the onset of damage within these structures is a challenging task that requires cutting-edge methodologies. Time-frequency analysis has proven its prowess in characterizing structural responses of nonlinear dynamical systems, facilitating the identification of subtle, yet critical, changes due to damage. This study delves deep into the use of the Generative Adversarial Network (GAN) for damage detection, with focus on the application to nonlinear dynamical systems through time-frequency analysis. GAN is an artificial intelligence technique that exhibits exceptional proficiency in data generation, synthesis, and detection of anomalies. By merging the valuable information provided by time-frequency analysis about nonlinear dynamical systems with the discriminative capability of the GAN, this works illustrates a novel approach towards a reliable damage detection in engineering structures and showcases its potential for SHM applications.