[Report] 15-Year Veteran Field Mechanic Becomes AI Talent... Korean Air's Experiment [AI Era, Jobs Are Changing]

Launched in August 2023, the Only Team in Korea’s Aviation Industry
Proactive Maintenance Approach Enhances Both Safety and Cost Efficiency
Over 2,500 Sensors per Aircraft, Generating 62GB of Data Daily
Detecting Anomalies in Advance Using Big Data and AI
Internal Staff Retrained to Develop AI Skills
Mechanics and Engineers Join the Team After Studying Coding

Editor's NoteThe use of artificial intelligence (AI) in industrial settings, including manufacturing, is expected to become the biggest topic in the industry this year. AI, which has already been introduced to manufacturing sites, has proven to play a pivotal role in boosting productivity. This year, its scope will expand significantly. However, concerns about job losses lie beneath the surface of AI-driven production innovation. As the so-called 'Dark Factory' concept-where production occurs without human intervention-becomes increasingly realistic, full automation is seen as a possibility. Yet, The Asia Business Daily’s investigation into job changes at manufacturing sites adopting AI reveals a more complex picture. Repetitive and hazardous tasks are being handled by machines, while humans are taking on roles involving judgment, management, and responsibility, resulting in a clear division of labor. Rather than replacing people, AI is beginning to redefine jobs. The AI era brings not only challenges in productivity and technological competition but also the task of job transition. The Asia Business Daily visited industrial sites in the new year to observe firsthand how AI is transforming the job landscape.

On the 9th of last month, at the Korean Air maintenance hangar in Jung-gu, Incheon, the Korean Air predictive maintenance team and a technician are inspecting the temperature sensor installed at the engine intake of an A330 aircraft. Photo by Kang Jinhyung

"The mechanics often ask the predictive maintenance team if we have some kind of 'supernatural power.' They wonder how we can identify defects days in advance without even seeing the aircraft in person."

On December 9, at Korean Air’s headquarters in Gangseo-gu, Seoul, an alert appeared on the computer monitor of Park Changhoon, Deputy Manager of the Predictive Maintenance Team. The alert indicated that cabin pressure was dropping on a passenger aircraft headed for Gimpo. Sensors installed on the aircraft detected that the rubber connectors between the ducts (the hoses that circulate air in the cabin) had become loose. If left unaddressed, this could result in the duct connectors tearing during operation a week later, potentially preventing sufficient air from being supplied to the cabin. Park analyzed relevant data, including previous maintenance records, and sent a work request to the field maintenance team advising them to replace the duct connectors.

He said, "We were able to catch a risk that could have led to a return or cancellation of the flight early, allowing us to perform maintenance before the next flight. Recently, similar defects have occurred three or four times due to aging parts, which helped us quickly identify the cause of the pressure drop."

On the 9th of last month, at the Korean Air maintenance hangar in Jung-gu, Incheon, the Korean Air predictive maintenance team and a mechanic are inspecting the temperature sensor installed at the engine intake of an A330 aircraft. Photo by Kang Jin-hyung

Predictive maintenance is a proactive approach that uses big data and artificial intelligence (AI) to detect and address potential issues in aircraft before they occur. Korean Air equips each aircraft with more than 2,500 sensors, generating an average of 62 gigabytes (GB) of data per day-equivalent to the content of 63,000 e-books. Using an in-house AI-based fault prediction model, the company analyzes this data, and when signs of anomalies are detected, it assesses whether maintenance is needed and then instructs the relevant work to be performed.

Predictive maintenance is more efficient than traditional maintenance methods because it helps avoid the time and costs associated with aircraft defects. If a defect is discovered during pre-flight maintenance, it often results in delays or cancellations. Additional expenses are incurred for accommodations and alternative flights during repairs. However, predictive maintenance allows defects to be anticipated about a week in advance, giving the company time to prepare for necessary maintenance and reduce costs.

On the 9th, at the Korean Air Maintenance Hangar in Jung-gu, Incheon, Korean Air predictive maintenance engineers and mechanics are inspecting the temperature sensor installed on the engine inlet of the A330 aircraft. Photo by Kang Jinhyung

In response, Korean Air established a dedicated Predictive Maintenance Team in 2023. Unique within the domestic aviation industry, this team identified over 100 defects in advance this year alone, resulting in cost savings of several billion won.

For example, the Predictive Maintenance Team once used visual sensors to detect abnormal signs in the landing gear. After takeoff, the aircraft wheels, which should have folded up to a 90-degree angle, were flying in a bent position. Since this area requires lifting the entire fuselage for inspection, it would have been difficult to detect the defect early without predictive maintenance technology. If the wheel angle had increased further in subsequent flights, the wheels might not have deployed during landing, making a return flight unavoidable. The aircraft in question was a large model with 90 business-class seats and a total capacity of 300 passengers, so a return flight would have resulted in significant costs.

Oh Jonghoon, Head of the Predictive Maintenance Team at Korean Air, is being interviewed at the Korean Air Maintenance Hangar in Jung-gu, Incheon on the 9th of last month. Photo by Kang Jinhyung

The significance of the Predictive Maintenance Team lies in the fact that its members were not recruited externally, but rather retrained from existing staff such as mechanics and engineers who had previously worked on-site, equipping them with AI skills. Oh Jonghoon, Head of the Predictive Maintenance Team, said, "For our team, people who know aircraft well are always the top priority. Because a deep understanding of aircraft systems is essential, we prefer to bring in employees from our internal pool who are interested in AI, rather than hiring AI talent from outside."

In particular, mechanics with years of hands-on experience working directly with aircraft are highly valued. Park, too, was a Korean Air mechanic with 15 years of experience. When the airline suspended operations during the COVID-19 pandemic, he began studying programming in line with the 'coding boom.' His skills quickly improved to the point where he could create websites and programs for work, and he joined the Predictive Maintenance Team when it was formed in August 2023. Park said, "While working as a mechanic, I wondered how I could reduce repetitive tasks and work more efficiently. I became interested in coding when I encountered data automation through programming. After the pandemic, when I heard that a Predictive Maintenance Team was being formed, I wanted to join."

On the 9th of last month, at the Korean Air maintenance hangar in Jung-gu, Incheon, the Korean Air predictive maintenance team and a technician are inspecting the temperature sensor installed on the engine inlet of an A330 aircraft. Photo by Kang Jinhyung

Kim Jaemin, a manager who started as a Korean Air engineer, was previously responsible for analyzing the causes of technical problems during aircraft operation and developing preventive measures. Kim said, "Predictive maintenance appealed to me because it involves anticipating and resolving problems before defects occur, rather than after the fact. I studied coding in my personal time after work, striving to create synergy with the practical know-how I had accumulated in the field."

Korean Air has also taken steps to promote the coexistence of AI and its employees by providing support. This year alone, the Predictive Maintenance Team attended 10 international conferences and global meetings. Oh said, "Overseas airlines adopted predictive maintenance technology before us, and some have already commercialized their own solutions. By seeing advanced technology firsthand and talking with developers, we are enhancing our technical capabilities."