For any business dealing with capital-intensive assets, maintaining machines and equipment is a continuous battle. From damaged equipment and undetected malfunctions to total machine failure that leads to unexpected (and costly) downtime, knowing how to future-proof your assets is a challenge. If your strategy is to only fix when something breaks, you're in a reactive mode, which often costs more and leads to extended, unplanned downtime, ultimately impacting your bottom line. Instead, what if you could know ahead of time when an asset will fail, and you could monitor your assets' health over time to determine the ideal frequency for maintenance? Predictive maintenance has emerged as a game-changing solution for asset management.
For scale, consider the Statue of Liberty, standing 305 feet tall. At 466 feet, the average wind turbine in the U.S. dwarfs Lady Liberty by more than half. And when GE's next-generation monster wind turbine, the Haliade-X, hits the market in 2021, it will nearly double that size to 877 feet, just shy of the Eiffel Tower. A single Haliade-X rotor blade will stretch 315 feet, longer than a football field. As a general rule of thumb, when it comes to energy and energy exploration, bigger is better: the larger the machinery, the deeper the dig, the greater the production yield.
Join our XPotential Community, future proof yourself with courses from XPotential University, connect, watch a keynote, or browse my blog. The world of maintenance might sound dull, but when the aircraft of the future have autonomous robot snakes and cockroaches from Rolls Royce fixing them all of a sudden things get a little bit more interesting. Now, in another giant leap forward for robot bug-kind a company called BladeBUG in the UK have unveiled a bug-like robot that, like human wing walkers, performs "blade walks" along the blades of operational offshore wind turbines. "[The new robo-bugs] open the door to autonomous inspection and repair of wind turbines, improving the efficiency of the blades and reducing risk for rope access technicians," said Chris Cieslak, founder and director of BladeBUG. "[Our robot] uses a patent-pending six-legged design with suction cup feet, which means each of the legs can move and bend independently. This is significant because it enables the robot to walk on the blade's changing curved surface, as well as inside the blade, tower, or hub of the turbine."
Turbine maintenance is an expensive, high-risk task. According to a recent analysis from the news website, wind farm owners are expected to spend more than $40 billion on operations and maintenance over a decade. Another recent study finds by using drone-based inspection instead of traditional rope-based inspection, you can reduce the operational costs by 70% and further decrease revenue lost due to downtime by up to 90%. This blog post will present how drones, machine learning (ML), and Internet of Things (IoT) can be utilized on the edge and the cloud to make turbine maintenance safer and more cost effective. First, we trained the machine learning model on the cloud to detect hazards on the turbine blades, including corrosion, wear, and icing.
The two biggest societal challenges for the twenty-first century are also the biggest opportunities – automation and climate change. The epitaph of fossil fuels with its dark cloud burning a hole in the ozone layer is giving way to a rise of solar and wind farms worldwide. Servicing these plantations are fleets of robots and drones, providing greater possibilities of expanding CleanTech to the most remote regions of the planet.