Researchers at Sandia National Laboratories have partnered with businesses to develop machines that non-invasively inspect wind turbine blades for hidden damage.
The research is part of the U.S. Department of Energy’s (DOE) Blade Reliability Collaborative work, funded by the DOE’s Wind Energy Technologies Office.
In one project, Sandia outfitted a crawling robot with a scanner that searches for damage inside wind blades. In a second series of projects, Sandia paired drones with sensors that use the heat from sunlight to detect damage.
“Wind blades are the largest single-piece composite structures built in the world – even bigger than any airplane – and they often get put on machines in remote locations,” says Joshua Paquette, a mechanical engineer in Sandia’s wind energy program. “A blade is subject to lightning, hail, rain, humidity and other forces while running through a billion load cycles during its lifetime, but you can’t just land it in a hanger for maintenance.”
Traditionally, the wind industry has had two main approaches to inspecting wind blades, Paquette says. The first option is to send someone out with a camera and telephoto lens. The inspector moves from blade to blade snapping photos and looking for visible damage, such as cracks and erosion. The second option is similar, but instead of standing on the ground, the inspector rappels down the tower or maneuvers a platform on a crane up and down the blade.
“In these visual inspections, you only see surface damage,” Paquette says. “Often though, by the time you can see a crack on the outside of a blade, the damage is already quite severe. You’re looking at a very expensive repair, or you might even have to replace the blade.”
These inspections have been popular because they are affordable, but they miss out on the opportunity to catch damage before it grows into a larger problem, Paquette says. Sandia’s crawling robots and drones are aimed at making non-invasive internal inspection of wind blades a viable option for the industry.
Crawling robots
Sandia partnered with International Climbing Machines and Dophitech to build a crawling robot inspired by the machines that inspect dams. The robot can move from side to side and up and down a wind blade, like someone mowing a lawn. On-board cameras provide real-time, high-fidelity images to detect surface damage, as well as small demarcations that may signal larger, subsurface damage. While moving, the robot also uses a wand to scan the blade for damage using phased array ultrasonic imaging.
The scanner works much like the ultrasound machines used by doctors to see inside bodies – except in this case, it detects internal damage to blades by sending back a series of signals. Changes in these ultrasonic signatures can be automatically analyzed to indicate damage.
Dennis Roach, Sandia senior scientist and robotic crawler project lead, says that a phased array ultrasonic inspection can detect damage at any layer inside the thick, composite blades.
“Impact or overstress from turbulence can create subsurface damage that is not visually evident,” Roach explains. “The idea is to try to find damage before it grows to critical size and allow for less expensive repairs that decrease blade downtime. We also want to avoid any failures or the need to remove a blade.”
Drones
Sandia worked with several small businesses in a series of projects to outfit drones with infrared cameras. This method, called thermography, can detect damage up to a half-inch deep inside the blade.
“We developed a method to heat the blade in the sun and then pitch it into the shade,” Sandia mechanical engineer Ray Ely says. “The sunlight diffuses down into the blade and equalizes. As that heat diffuses, you expect the surface of the blade to cool. But flaws tend to disrupt the heat flow, leaving the surface above hot. The infrared camera will then read those hot spots to detect damage.”
Ground-based thermography systems are currently used for other industries, such as aircraft maintenance. Because the cameras are mounted on drones for this application, concessions have to be made, Ely says.
“You don’t want something expensive on a drone that could crash, and you don’t want a power hog,” Ely explains. “So, we use really small infrared cameras that fit our criteria and use optical images and LiDAR to provide additional information.”
LiDAR, which is like radar but with light instead of radio frequency waves, measures how long it takes light to travel back to a point to determine the distance between objects. Taking inspiration from NASA’s Mars lander program, the researchers used a LiDAR sensor and took advantage of drone movement to gather super-resolution images.
“You use the movement to fill in additional pixels,” Ely says. “If you have a 100- by 100-pixel camera or LiDAR and take one picture, that resolution is all you’ll have. But if you move around while taking pictures, by a sub-pixel amount, you can fill in those gaps and create a finer mesh. The data from several frames can be pieced together for a super-resolution image.”
Using LiDAR and super-resolution imaging also makes it possible to precisely track where the damage on a blade is, and LiDAR can also be used to measure erosion on blade edges, the researchers say.
The future
Autonomous inspections of bridges and power lines are already realities, and Paquette believes they also will become important parts of ensuring wind blade reliability.
“Autonomous inspection is going to be a huge area, and it really makes sense in the wind industry, given the size and location of the blades.” Paquette says. “Instead of a person needing to walk or drive from blade to blade to look for damage, imagine if the inspection process was automated.”
Paquette says there is room for a variety of solutions and inspection methods, from a simple ground-based camera inspection, to drones and crawlers, all working together to determine the health of a blade.
“I can envision each wind plant having a drone or a fleet of drones that take off every day, fly around the wind turbines, do all of their inspections, and then come back and upload their data,” Paquette concludes. “Then the wind plant operator will come in and look through the data, which will already have been read by artificial intelligence that looks for differences in the blades from previous inspections and notes potential issues. The operator will then deploy a robotic crawler on the blade with suspected damage to get a more detailed look and plan repairs. It would be a significant advance for the industry.”
