AI-Driven Predictive Maintenance for Aircraft

The global aircraft fleet is expected to reach 38,0000 by 2033. Since the maintenance of every piece of aircraft is costly and complex, adopting AI in aviation is inevitable.

Today, industry leaders like Boeing, Airbus, and Lufthansa rush to implement AI in aircraft maintenance procedures and build up massive subdivisions like Lufthansa Technik that implement cutting-edge innovations in their fleet lifecycle. For example, Lufthansa started VR and AI-based performance check-ups in many of their airplanes, while Airbus uses 3D simulations to test aircraft safety.

In this article, we’ll give a general overview of AI in the aviation industry in general, and describe the specific benefits of AI in aircraft prediction maintenance.

Let’s get started!

How is AI used in aviation?

The role of AI in aviation in general and aircraft maintenance in particular grows. No one can doubt that.

The aviation industry uses AI to optimize flight routes and schedules, personalize pilot training, and enhance customer experience, yet what is most important is to transform aircraft maintenance.

The shift towards AI-powered predictive aircraft maintenance started a decade ago, yet it got a clear outline in 2018. Here’s what changed that year: 

Before, companies followed a preventive maintenance approach. Repairments took place after something went out of order. Aircraft maintenance engineer teams consisted of numerous specialists doing a lot of work manually. Diagnostics took the lion’s share of the maintenance process.

Now, aviation has shifted towards a predictive maintenance approach. Repairments mostly take place before something goes out of order. Intelligent predictive mechanisms based on artificial intelligence and smart sensors alert of possible malfunction in advance so that an aircraft engineer can make razor-sharp manipulations to fix potentially weak links before an actual breakdown.

The shift to broader use of AI in the aviation industry promises a decrease in unscheduled maintenance instances in the following years. For example, the aircraft maintenance, repair, and overhaul market in the Asia-Pacific region, as of 2024, is growing at an annual rate of 2.8%. Yet, after 2028, the growth rate is predicted to slow down to 0.7% a year.

Aviation companies will spend less on unpredicted maintenance and repair. Let’s take a closer look at the role of AI in this process.

AI in aircraft maintenance scheduling and reporting

Aircraft maintenance documentation is extensive. Pilots, aircraft maintenance engineers, and operators invest a lot of time doing paperwork, like noting down and reporting breach instances. Situations when critical details are omitted are not uncommon. More importantly, some critical breakdowns get unspotted, like with Boeing’s 737 MAX in 2018 and 2019 and again on Jan 5 this year.

How does AI help?

The role of AI in aircraft maintenance scheduling is simple yet vital. AI algorithms keep track of essential maintenance schedules and automate alerts to operators and engineers to ensure that all regular maintenance check-ups are noticed.

Optical character recognition (OCR) and intelligent document processing (IDP) tools are also essential here. They capture, extract, and digitize data from maintenance reports (usually printed forms filled in manually by engineers). This doesn’t just make maintenance reporting faster but also prepares data for further AI check-ups and analysis.


Interested to learn how OCR and intelligent document processing reduce routine labor?

Proceed to our article Enhancing Efficiency in Logistics: The Role of OCR and AI

Automated performance monitoring

Performance monitoring collects data from various sources (engines, electronics, etc.) to detect if everything works as expected.  

Before data collection was partially automated, performance reports were complicated and unsuitable for AI data analysis. Thus, important trends were skipped.

Now, AI algorithms allow us to capture more data, structure them, and pinpoint essential trends that could be otherwise skipped. Thus, if performance fluctuates outside expectations, AI alerts maintenance professionals faster, and they investigate mechanical problems sooner, which makes regular inspections more focused.

Industry example: Airbus has developed an AI/ML platform to automate performance monitoring. The Skywise platform presents a perfect example of AI in aircraft maintenance. The system analyses abnormal behavior from sensor-captured data. It further alerts the findings, which help aviation companies shift from major to minor repairs and save costs.

Anticipating mechanical failure with intelligent sensors and AI

Aircraft maintenance teams depended on sensors for a long time. Yet before, the data collected by sensors wasn’t complete, and many things depended on visual check-ups. Also, sensors were primarily responsible for data capture, while other systems performed the analysis manually or manually, which took time. Often, the maintenance team paid attention to the problem as it already occurred.

The use of AI in aviation, mainly in aircraft predictive maintenance, has changed the game. Intelligent algorithms process a larger data feed, so minor abnormalities are spotted sooner. The issues get addressed before they evolve into something bigger.

AI computer vision systems for aircraft visual inspection

Computer vision algorithms are used to scan aircraft for signs of malfunction. With the use of computer vision and AI in aviation, inspection processes have become faster, more frequent, and more efficient. With this, aircraft maintenance engineers get more time for inspection, and the aircraft gets back into the sky sooner.

Industry example 1: Fujifilm has developed diagnostic imaging software, which they call DynamIx VU, to take high-precision images of different aircraft parts, such as welds, fuel tanks, rotors, etc. Once AI trains to spot signs of malfunction, the issues get detected and reported rapidly.

Industry example 2: Lufthansa uses a combination of intelligent cameras, drones, and artificial intelligence to provide visual check-ups of their commercial aircraft. Their technology was first presented at the ITS World Congress in Hamburg and has been successfully utilized since 2021.

The use of AI in aircraft maintenance data analysis

AI is best at spotting hidden trends in big data sets, and aviation is the best industry to offer a considerable ground for work. Invaluable insights about aircraft maintenance are hidden in tons of data and may be too specific for a particular fleet to be spotted using general guidelines.

The implementation of AI data analysis helps airlines find and remove dependencies that can bring issues in aircraft operations. For example, companies may establish the dependencies between fuel consumption and specific weather conditions or find out how the implementation of certain spare parts leads to improvement or reduction in productivity.

Summing up

Any of the applications of AI in aircraft maintenance eventually leads to preventing major breakdowns and helps to avoid costly reparations, which ends up in economy. As Lufthansa’s Senior Director of Analytics and Data Solutions, Jan Stoevesand said: “The real savings are in the avoidance of operational instances. That’s where the money is.”

In the future, maybe the closet one, we are to expect even more effective ways to save the cost and enhance predictive maintenance.


If you want to know how to benefit from the advantages of AI in aviation, contact eNest for a free consultation and advice!

Our team will analyze your business case and will offer solutions that will help you to solve it. Book a call now! 


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Jagdeep Chawla

MS in Data Science
NorthWestern Univeristy, Illinois

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