Wind Turbine Blade Post Infusion Inspection System
My primary responsibility on the team was sensor integration. I began by developing an interface between the robot’s main controller and a drone camera API to enable simultaneous image capture for environmental analysis. This required capturing images along with metadata—distance readings from two sensors, camera ID, and timestamp—within 0.2 seconds, the time it takes the system to move one meter. To reduce image setup time, I disabled the camera’s default auto-refocus except at the start of movement. Although this improved performance, file transfer remained a bottleneck. After reviewing the hardware, I replaced USB 2.0 cables with USB 3.0 to increase transfer speed. I then implemented multithreading to allow all cameras to capture images simultaneously. This reintroduced transfer delays, which I resolved by deferring image transfers until the end of the inspection. Metadata was logged before each image set and later merged with the images prior to saving to the SSD.
My second task involved mechanical design. The original actuator homing method relied on torque sensors, which proved unreliable. I was assigned to develop custom limit switch mounts to detect when the robot was homed. Due to constraints on modifying the system, I designed sensor attachments that required minimal changes. One actuator had a wide range of motion, making a standard limit switch impractical. To address this, I created a flexible lever arm that would trigger the switch when homed. Another actuator lacked space for a switch beneath it, so I designed a small extension arm that reached an open area where the switch could be mounted.
The system was tested at a GE Vernova wind turbine blade factory. Compared to traditional post-infusion inspection methods, workers identified more defects and reported reduced physical strain. Based on these results, the system is now being refined for deployment at additional GE Vernova facilities.