The enterprise drone market is ascending rapidly. Goldman Sachs estimated that businesses will spend $13 billion on drones between now and 2020. Promising commercial applications for drones range from emergency response and firefighting to surveying farmland and grocery delivery. However, as is the case with any new and innovative technology, there have been some speed bumps along the way that must be delicately navigated before broad adoption sets in.
One of the most common speed bumps for businesses is the challenge of analyzing the vast volumes of data collected by drones. Data is only valuable if we’re able to derive meaningful insights that we can use to make more intelligent business decisions and operational or process improvements. To overcome these hurdles and realize the true potential that drones can provide in the commercial sector, I believe we must stop thinking in terms of “man vs. machine” and start thinking about the best ways to use man and machine together — to maximize output, minimize costs and reduce inefficiencies.
The 27.4 billion opportunity
Take the wind power industry as an example. The global wind energy market experienced double-digit annual growth over the last decade, but the high cost of operations and maintenance (O&M) on wind farms is an obstacle for further growth, particularly as wind turbines age. The O&M market for wind energy is expected to reach $27.4 billion by 2025, yet there are still very few technologies in the market that help wind farm operators improve operational decision-making. Additionally, many critically important O&M tasks, such as wind turbine inspections, remain mostly manual, frustratingly time-consuming and sometimes even dangerous.
Wind turbine blades must be inspected regularly to identify problems, such as cracks or chips in the blades. If wind farm operators don’t catch these problems early, they often need to shut down the turbine for days or weeks at a time to replace the entire blade — costing hundreds of thousands of dollars, not to mention the lost revenue as a result of downtime and not producing electricity. Traditionally, blade inspections have been performed by highly trained technicians who must scale the massive turbines on ropes — a precarious position even in the best of weather. Though wind turbine service technicians are one of the fastest-growing occupations in the U.S., there simply aren’t enough people on this planet willing to scale a turbine more than 60 meters (200 feet) into the air to perform these tasks. Despite the growing interest in the field, the human capital to throw at this problem will not be able to keep pace with the explosion of new turbines being built — more than 341,000 around the world and counting.
To keep the wind power industry on this upward trajectory, wind farm operators must look to ways they can use emerging technologies, such as autonomous drones, computer vision, machine learning and data analytics, together with the experience and skills of their human technicians, to reduce costs, improve efficiency and increase profits.
‘Man and machine’ or ‘man vs. machine’
In the case of wind turbine inspections, technicians are turning to autonomous drones to help speed up inspection process and gather data they can use to improve turbine performance and profitability.
With the click of a button, technicians can send a drone up the turbine to conduct a detailed visual inspection that is simply unachievable by humans. Using computer vision and precision photography, the drones can automatically track a path along each blade, taking thousands of photos and identifying defects, such as cracks or chips as small as 1 millimeter by 3 millimeters. Machine learning automatically stitches together the photos to give technicians a holistic view of the entire blade via a cloud-based portal. Whereas a manual inspection could take six to eight hours per turbine, for technicians armed with autonomous drones, computer vision and machine learning, wind turbine inspections can be completed in as little as 15 minutes.
More than just helping speed inspections, AI can help wind farm technicians better perform their jobs by improving decision-making and maintenance prioritization processes. By applying machine learning to the vast amounts of data captured from the drones’ visual inspections, technicians can better understand the growth rate of defects, see the progression of damage and track the lifecycle of not only individual turbines, but also the entire fleet. Overlay that data with information on climate, terrain and the amount of rain or lightning hitting the area and technicians suddenly have at their disposal a prescriptive and predictive health analysis of the entire wind farm. Rather than conducting inspections based on a time schedule or after a problem has been detected, wind farm technicians can use AI to predict when faults will occur and prioritize maintenance based on the severity and progression trend of a defect. The wind farm’s AI and machine learning software can even be connected to maintenance vendors, insurance companies and work order systems to automatically schedule preventive maintenance in advance and avoid costly downtime. It can automatically take into account how much lead time the maintenance company will need, how fast the wind turbine’s defect will deteriorate, how much money is available for maintenance work and more to schedule the optimum maintenance schedule based on all these factors.
In short, empowered with technologies like autonomous drones, AI and data analytics, wind farm operators and technicians can not only see the smallest hairline crack on a turbine blade, but also zoom out to understand the big-picture view and trend analysis of the entire operation in order to make operational changes that can extend the life of the turbines and increase the overall efficiency and profitability of the wind farm.
Business is best when man and machine work together
The need for the O&M sector to keep pace with the explosive growth of wind energy farms around the world is forcing a digital revolution, resulting in numerous astounding technological breakthroughs. While there are certain aspects of the O&M industry that will likely never be replaced by machines and software, there is a tremendous opportunity to augment those roles to improve the overall outcome. Working together, man and machine can automate time-intensive and potentially dangerous operational tasks and use real-time data to streamline operations and improve efficiency for greater profitability. By augmenting the arduous, manual process of inspecting and servicing wind turbines with cutting-edge technology, the industry can enjoy the best of both worlds.
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