A Dual-Mode Millimeter-Wave Sensor Network for Structural Monitoring in Wind Farms

This project will enhance the resiliency and sustainability of the Nation’s wind energy infrastructures by investigating effective and autonomous turbine inspection. The major forms of structural monitoring of wind structures have traditionally been accomplished using contact-based sensors such as accelerometers and strain gauges. However, application of these contact-based sensors is limited by challenges in onsite integration, maintenance, and reconfiguration.

Light Detection and Ranging (LIDAR) systems are non-contact and have high precision, but they are susceptible to weather conditions and are often installed at fixed locations. Cameras can be mounted on unmanned aerial vehicles (UAVs) to scan the surface of turbines. However, this approach requires stopping the rotation of turbine blades and can only inspect the surface.

To address these challenges, this project will develop a dual-mode millimeter-wave sensor network driven by a unified decision-making framework that optimizes structural inspection in wind farms. Since radio frequency signals are robust against ambient light and weather conditions, a stationary platform located near turbines will offer robust continuous monitoring during normal operation.

On the other hand, a formation of swarm-UAV-based sensor network can synthesize a large observation aperture for high-resolution imaging when an initial problem is identified by continuous-monitoring sensors, or during scheduled maintenance. This project has an interdisciplinary nature involving millimeter-wave sensing, adaptive UAV formation, swarm flight control, and unified decision-making framework for system-level inspection schedule optimization.

It will generate new knowledge and methodologies for structural health monitoring of critical infrastructures such as power transmission networks, oil/gas pipelines, and transportation networks. The project provides a valuable opportunity for students to develop their interest in the fields of system reliability optimization, autonomous robotics, and microwave/millimeter-wave technologies.

The PIs will develop integrated research and education programs to attract students from underrepresented groups and K-12 students into engineering and involve undergraduate students into research. It will also encourage student entrepreneurship based on successful technology development.

The project is innovative in that it integrates unique miniature millimeter-wave remote sensing capability with advanced UAV control methods for networked coherent detection. Furthermore, it unifies the long-term inspection planning at the system level and the dynamic prognosis based on the short-term information at the turbine level. This project will investigate: 1) stationary sensors mounted near wind turbines to provide uninterrupted monitoring while turbines are in operation, where analytic and machine learning methods will be integrated to analyze blade distortion from the micro-Doppler signatures generated by rotating turbine blades; 2) UAV-based sensor arrays to scan details of turbine blades with high-resolution synthetic aperture imaging; 3) novel intrinsic flight control strategies to enable swarms of UAVs to realize the desired formation with sufficient precision, energy efficiency, and minimal jitter for synthetic aperture imaging; 4) a unified decision-making framework to optimize the inspection schedule of wind turbines from a systems and dynamic perspective.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.