Experimental Validation of UAV-Deployed Edge Sensors for Frequency-Based Bridge Damage Detection

Abstract

This work presents the experimental validation of a UAV-deployable wireless sensor package capable of edge-based Fast Fourier Transform (FFT) processing for frequencybased Structural Health Monitoring (SHM). The sensor system executes onboard computation to detect shifts in natural frequencies, enabling rapid assessment without relying on continuous high-bandwidth communication. A lab-scale beam structure is used to simulate structural state changes by repositioning support locations, with a UAVdeployable sensor placed on the beam to capture vibration data alongside a reference accelerometer. The embedded FFT algorithm performs local signal transformation and peak detection, successfully identifying modal frequency shifts correlated with altered boundary conditions. These peak frequencies are logged and prepared for selective wireless transmission to reduce power and data transmission demands. The sensor package is shown to reliably detect frequency shifts of approximately 1 Hz across configurations. This demonstration underscores the viability of low-power, autonomous edge-sensing systems for scalable, rapid-deployment SHM platforms, particularly when structural access is constrained or latency is critical.