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Drone to fill the sky, UAM...Check the fault in 10 seconds with AI.
2024-05-31
WEFLO Develops Unmanned Inspection System
AI measures electromagnetic waves, vibrations, and noise
Fault reading…It's over 98% accurate.

To be provided to 23 places including Jeju-si and Doosan.

With the era of future mobility such as urban air transportation (UAM) and drones approaching, domestic startups are drawing attention by developing drone unmanned inspection solutions using artificial intelligence (AI). It is possible to check the condition of parts inside the aircraft in seconds that cannot be confirmed with the naked eye.

According to the drone industry on the 31st, startup WEFLO has developed a drone inspection system called "Bertifit" and is providing it to 23 locations nationwide. verti-Pit is the first automatic inspection solution in Korea based on AI, driving unit and gas unit sensors, and big data. It is used by domestic and foreign companies and local governments such as Doosan Digital Innovation, Marine Drone Technology, Pablo Air, Seongnam City, Gyeonggi Province, and Jeju City. WEFLO is a startup founded in 2022 by CEO Kim Eui-jung, a Ph.D. information and communication engineering at KAIST and a former Hanwha System.

Before the drone takes off, the detection sensor measures physical quantities such as electromagnetic waves and wake (air flow due to blades), noise, and vibration of the aircraft to check for failure. This is how AI perceives it as a failure if a certain physical quantity value is out of the normal range. It is possible to identify blade damage, wear of motor bearings that cannot be confirmed with the naked eye, disconnection of coils, and bolt fastening strength. The accuracy is more than 98%. WEFLO has 23 related patents at home and abroad.

The flight inspection takes less than 10 seconds. Previously, two inspectors had to check the aircraft for at least 10 minutes. It was only at the level of visually checking the blade and motor. "The current inspection system still has the risk of accidents because it relies entirely on labor," CEO Kim said. "The inspection system using AI not only drastically reduces labor costs and inspection time, but also significantly lowers the risk of accidents such as falls."

It also developed predictive maintenance technology using AI. It is a technology that analyzes the physical quantity pattern of an aircraft based on big data and predicts what kind of failure will occur at a specific time in the future. In addition to drones, WEFLO has UAM maintenance technology that allows people to board. Typical examples are a "gas health monitoring system" that checks internal equipment of the aircraft and a "light maintenance system" that maintains the appearance of the aircraft. Until now, UAM has not been commercialized, so the service has not been supplied.
As UAM is commercialized and the market grows, the demand for flight inspection is expected to increase. Morgan Stanley, a global investment bank, predicted that the global UAM market will grow to $744 billion (about 1,030 trillion won) by 2036, nearly 80 times larger than this year's $9.6 billion.

Reporter Jang Kang-ho: callme@hankyung.com


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