UTA Researchers Develop Radar-Based System To Predict Drone Failures At Low Cost

As drone fleets expand across commercial and industrial sectors, preventive maintenance is emerging as a critical challenge for operators. A team at the University of Texas at Arlington (UTA) is addressing this issue by developing a low-cost radar-based system designed to detect mechanical anomalies in unmanned aerial vehicles (UAVs) before they lead to failures. Led by Dr. Dianqi Han, assistant professor in the Department of Computer Science and Engineering, the project uses millimeter-wave radar to monitor key operational parameters such as propeller rotation speed, vibration intensity, and flight trajectory to flag early signs of wear or damage in real time.

The proposed system is lightweight and scalable, relying on millimeter-wave radar technology that allows a single unit to monitor the mechanical health of multiple drones simultaneously. Unlike traditional manual inspections, which are still the norm in the industry, the UTA system enables continuous, remote diagnostics from distances exceeding 100 meters. Han emphasizes that as drone fleets age and scale up, the current reactive approach—where failed drones are simply replaced—will no longer be cost-effective or operationally sustainable. The new system aims to shift the paradigm toward predictive maintenance, extending operational lifespans and reducing downtime.

The radar technology chosen for this system, developed by Texas Instruments, is both affordable and powerful. It can detect slight variations in mechanical performance that typically precede serious malfunctions. This includes detecting unbalanced rotors, decaying motor speed, or early structural weakness in drone joints. According to Han, the full monitoring system, including a radar unit and a basic laptop for processing, could be deployed for under $600.

This makes the system particularly attractive for drone delivery companies and operators managing large autonomous fleets, where any unexpected failure can disrupt operations at scale. The system is currently undergoing testing in controlled environments.