This study aims to develop a structural health monitoring model that autonomously assesses breathing-type debonds between the base plate and stiffener in lightweight composite structures. The approach utilizes a specifically designed deep learning architecture that employs nonlinear ultrasonic signals for automatic debond assessment. To achieve this, a series of laboratory experiments were conducted on multiple composite panels with and without base plate-stiffener debonds. A network of piezoelectric transducers (actuators/sensors) was used to collect time-domain guided wave signals from the composite structures. These signals, representing nonlinear signatures such as higher harmonics, were separated from the raw signals and transformed into time-frequency scalograms using continuous wavelet transforms. A convolutional neural network-based deep learning architecture was designed to extract discrete image features automatically, enabling the characterization of composite structures under healthy and variable breathing-debond conditions. The proposed deep learning-assisted health monitoring model exhibits promising potential for autonomous inspection with high accuracy in complex structures that experience breathing-debonds.
|Title of host publication
|Structural Health Monitoring 2023
|Subtitle of host publication
|Designing SHM for Sustainability, Maintainability, and Reliability - Proceedings of the 14th International Workshop on Structural Health Monitoring
|Saman Farhangdoust, Alfredo Guemes, Fu-Kuo Chang
|DEStech Publications Inc.
|Number of pages
|Published - 12 Sep 2023
|14th International Workshop on Structural Health Monitoring: Designing SHM for Sustainability, Maintainability, and Reliability - Stanford University, Stanford, United States
Duration: 12 Sep 2023 → 14 Sep 2023
Conference number: 14
|Structural Health Monitoring: International Workshop on Structural Health Monitoring
|14th International Workshop on Structural Health Monitoring: Designing SHM for Sustainability, Maintainability, and Reliability
|12/09/23 → 14/09/23