In 2026, air traffic at Charleroi and Liège airports will be remotely managed by a single digital control tower centre in based in Namur. The centre will oversee all ground movements during landing and take-off at the two airports.
Recently, SOWAER (Société Wallonne des Aéroports) and the Belgian air navigation service, skeyes, debuted the ‘Digital Tower Test Centre’ prototype at their site in Steenokkerzeel. The test centre is “almost identical” to the one currently being built in Namur and will be used to familiarise staff with the new technologies, train air traffic controllers, and deliver a seamless transition once the Namur centre is open. While the test centre is fed with real-time images from the masts in Liège and Charleroi, it does not have module for communicating with aircraft.
These digital towers are “the future for air traffic control,” using advanced cameras, infrared systems, and sensors at airports to receive real-time images on large screens at control centres showing a 360° view of the airport’s horizon. A ground radar system supplements this by pinpointing aircraft locations, even in poor weather. Augmented reality further enhances control by projecting extra information onto aircraft, aiding traffic management.
Eventually, the digital control centre in Namur will replace the physical towers at Charleroi and Liège airports. The innovative approach to air traffic control complements the wider digital transformations at the airports, illustrating their efforts to modernise infrastructure, streamline operations, and improve safety standards.
Korean Air has successfully completed the “world’s first” comprehensive urban air mobility (UAM) operations demonstration.
Partnering with Hyundai Motor Company, Korea Telecom (KT), Incheon International Airport Corporation (IIAC), and Hyundai Engineering & Construction, the South Korean flag carrier conducted five week demonstrations at Goheung Aviation Test Center in South Jeolla Province.
The urban air mobility (UAM) system relied on a 5G aviation communication network linking the electric vertical take-off and landing (e-VTOL) aircraft with the UAM operating systems. For the demonstrations, the airline conducted 10 different scenarios ranging from “normal to abnormal” during which they tested the functionality and performance of the operating systems.
Working to ensure safe UAM operations in high-density urban environments, the data collected from these tests will be analysed and the systems enhanced.
“Korean Air will continue to engage in various UAM initiatives, and work to validate and enhance government-established UAM concepts and procedures to develop the UAM ecosystem in Korea.”
Historically, augmented reality (AR) has opened up possibilities within the industry, especially around training and inspection. As the technology becomes more refined, its use cases evolve symbiotically. Recently, KLM Royal Dutch Airlines shared how they are using Apple’s Vision Pro to elevate technical maintenance and training.
In 2023, Apple debuted the AppleVision Pro, “a revolutionary spatial computer that seamlessly blends digital content with the physical world.” With AR, users can overlay digital information and interactive elements onto their surroundings while virtual reality (VR) offers users a fully simulated and immersive digital experience.
Its debut restarted excitement around the potential use cases of extended reality (XR) across industries. This month, Apple showcased how KLM Royal Dutch Airlines is using the Vision Pro. The airline’s Engine Shop app allows technicians to see a step by step breakdown of the task ahead of them with detailed instructions overlayed on an accurate 3D model of the exact engine.
Bob Tulleken, Vice President of Operations Decision Support, KLM said:
“We see Apple Vision Pro as a tremendous value-add that will improve our fleet availability and operations […] Training our employees with spatial computing will lead to fewer costly errors because the most current information they need to do their job is there in front of them as they perform the task. This means we not only get vastly more efficient in our work, but also provide a better work environment for our employees to succeed.”
Apple shared this short video demonstrating how the airline is leveraging spatial computing for technical maintenance. Video credit: Apple.
The Apple Vision Pro may have re-ignited excitement around XR but the aviation industry has been leveraging immersive technologies that merge the physical and virtual worlds in a multitude of ways for some years. As the technology evolves, it will open doors to more applications across the industry giving way to transformative advancements.
I think maintenance can be a little more efficient, don’t you? Maintenance Scheduling & Supply Chain Optimization
An application of AI that has many use cases is optimization, which includes maintenance scheduling optimization.
An optimization engine that can schedule maintenance at the best possible time at the best possible location has the potential to greatly reduce maintenance costs and improve maintenance yield fleet wide. At the same time, optimizing the order that are performed in, and how personnel are assigned to tasks can result in more efficient maintenance—reducing costs, improving turnaround time, getting the aircraft back in the air sooner—thereby generating more revenue.
Are you sure that’s correct? Error Detection & Reclassification
Another use of AI is to identify errors made in entering data, or to reclassify data after the fact to ensure accuracy of data and improve the overall quality of the dataset. A common problem across the airline industry is the misclassification of the failed ATA system when raising a fault. These misclassifications can impact the data quality in the system.
IFS Customer Southwest Airlines has rolled out a solution to use AI to identify misclassified faults and improve the overall quality of their data. This is an excellent application of LLMs that learn to identify patterns in the text entered by the technicians to classify faults more accurately. Using AI to identify potential errors and surface those potential errors to a person helps to make the whole process vastly more efficient while maintaining authorized human oversight.
Let’s try this again—Automated Failure, Troubleshooting, and Repair Identification
When a fault is raised, a technician is often required to spend a considerable amount of time researching the correct source of a fault, what troubleshooting steps to take and what repairs to apply.
The logical extension of fault classification is to take the same kind of LLM model which will suggest potential sources of the failure, and recommend troubleshooting activities or even make repair suggestions. Suggestions would be based on previous success rates.
By providing first time fix rate percentages to the technician, they may choose options that save time by resolving the issue quicker, meaning the aircraft may be able to get back in the air sooner, or even prevent recurrences in the future. Avoiding or reducing has real value, according to Airlines for America, in 2023, delays have a direct cost of $101.18 for every minute a flight is delayed.
Now what will happen? Predictive Maintenance & Anomaly Detection
The concept of predictive maintenance is nothing new. However, what is new is the application of newer types of AI, namely Anomaly Detection and Pattern Recognition. Predictive maintenance uses time-series data—which is gathered over long periods of time recoded at fixed known points along the way, i.e., discrete data points in time.
Live feeds of sensor data changed that with IoT, allowing current data to be considered—but that huge amount of data is difficult to interpret, requiring highly trained data scientists. Then Machine Learning (ML) came into the picture. Using ML means that data scientists are now focussed on creating the current learning model for the AI, rather than developing the algorithm themselves.
Unsupervised learning models are lowering the barrier to entry for the use of AI in predictive maintenance applications. “Unsupervised” learning models for AI means that you can plug the AI into a set of data and it can figure out its own algorithm. This reduces the time and cost of implementing a solution, and also has the power to remove bias from the process, particularly when dealing with large amounts of unlabeled data like multiple terabytes of data points generated from a modern aircraft .
Anomaly Detection means that you can plug the AI into the sensor data to figure out what “normal” looks like, it then warns you whenever a deviation from “normal” occurs. Coupled with Pattern Recognition, the AI can learn to detect patterns in the sensor data that indicate certain events are about to occur—providing an early warning system that can warn what is about to happen with extremely accurate results.
AI is here to stay
By using AI to streamline tasks, to provide decision support to the human, to pare down the noise of information—while keeping the human in the loop, still requiring them to be the ultimate decision maker—cutting edge companies have the ability to make huge strides in terms of efficiency and accuracy.
These improvements can represent real value to airlines & air operators and ultimately, their customers.
Article by Rob Mather, Vice President, Aerospace and Defense Industries at IFS
Earlier last week, Rolls-Royce announced it has successfully commenced the flight test campaign for the Pearl 10X. This signals the company’s growing focus in the business aviation market and is expected enable ultra-long range connections, whilst being close to travelling to the speed of sound.
Already, the engine has surpassed target thrust levels which makes it the most powerful business aviation engine in the Rolls-Royce portfolio.
In what will be a “market-leading combination of power and efficiency,” the engine is also built with environmental standards in mind featuring an ultra-low emissions ALM combustor, compatible with 100 per cent Sustainable Aviation Fuel (SAF).
Philipp Zeller, Senior Vice President Dassault, Business Aviation, Rolls-Royce, said:
“We are excited to enter into this important next phase of the engine development programme with the start of our flight test campaign. All the tests complete to date confirm the reliability of the engine and show it will meet the performance requirements to power Dassault’s flagship, the Falcon 10X.”
By last week, the programme had already hit over 2,300 testing hours and in the coming months pilots and flight test engineers will ascertain its performance at varying speeds and altitudes.
At ATW Europe, Adam Harris, Global Chief of Testing Facilities, Rolls Royce will be speaking on innovation and testing environment, unpacking advanced testing activities for sustainable aviation. Book your ticket now to avoid missing out!
According to McKinsey, intelligent computers are to the Fourth Industrial Revolution (Industry 4.0), what steam was to the first. Powered by disruptive technology, machine learning (ML), artificial intelligence (AI), and data analytics, Industry 4.0 is characterised by the merging of the digital and physical worlds.
“A technology based on the concept of creating a virtual replica or representation of a physical object, process, or system. It is fed with real-time or historical data, then this data is analysed through machine learning algorithms to run simulations of different environments to understand how a system is going to behave, the results of this simulations are shown in dashboards, reports or visualization tools, and decisions can be made based on that information.”
The application of this technology at Rolls-Royce demonstrates its various benefits to the industry. Here’s how it works with engines:
Our Engineers create a Digital Twin of an engine, which is a precise virtual copy of the real-world product. They then install on-board sensors and satellite connectivity on the physical engine to collect data, which is continuously relayed back to its Digital Twin in real time. The twin then operates in the virtual world as the physical engine would on-wing and will determine how the engine is operating and predict when it may need maintenance. This also allows us to enact preventative engine maintenance, which can greatly reduce aircraft downtime and, in turn, enhance reliability.
Just some of the key benefits include:
Accuracy – The model accurately reflects the condition of using real-time data, continuously learning and updating itself to reflect the real-life operating conditions.
Reliability – Any problems are flagged earlier which minimises disruption.
Testing – Using the digital copy, a larger number of potential circumstances can be tested than with physical tests, allowing engineers to simulate extreme conditions to better understand behaviours. Can also predict how engines will be operated by different airlines in a range of geographic regions to better understand the engine’s performance across the course of its lifetime.
Endurance – Lengthens the time between services, increasing the engine’s time on wing, in some cases the TOW for critical parts has been extended by over 70 per cent.
Planning – Maintenance can be optimised and planned accurately with up-to-date knowledge of how the engine is performing and when it will need attention.
At ATW Europe, Adam Harris, Global Chief of Testing Facilities, Rolls Royce will be speaking on innovation and testing environment, unpacking advanced testing activities for sustainable aviation. Book your ticket now to avoid missing out!
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