How AI is Changing the Aviation Industry
According to a recent Accenture report, 80% of leading executives within the aerospace and defense industries expect that every part of their workforce will be directly affected by AI-based decisions by 2021. This points to the immense impact AI and other digital technologies will have on the aerospace industry in the near future. From smart maintenance to training and more, here are 5 ways in which AI could transform the aerospace industry:
1. Smart maintenance
Maintaining aircraft is essential to ensuring the safety of the aircraft in question. Since maintenance is typically performed based on schedule, the process can be time-consuming and cumbersome. Unexpected faults or malfunctions can result in unnecessary downtime and the inefficient use of expensive engineering labor. Since 45% of industry professionals see resolving unexpected maintenance issues as a key way to drive efficiency, it’s unsurprising that aviation companies are increasingly looking into predictive maintenance, enabled by AI.
Predictive analytics sifts through the maintenance data, interpreting and organizing information from sensors and reports. This allows algorithms to identify and report on potential failures in real-time and predict the most suitable repair timelines, creating smarter maintenance schedules. Aerospace companies like Airbus are already looking to adopt smart maintenance solutions to predict variations in manufacturing processes, based on data from a range of factories.
SparkCognition is a company that provides machine learning solutions for a number of industries, including aerospace. For example, SparkCognition’s SparkPredict software helps to predict asset failures by monitoring the mechanical systems in an aircraft, whilst also being able to recommend best corrective actions.
Airbus has also recently launched its Skywise platform in partnership with Palantir Technologies, which analyses data to forecast aircraft technical issues. easyJet one airline that is already using the platform to reduce delays caused by unexpected maintenance-related issues. Skywise is said to have helped predict 31 technical failures across easyJet’s fleet, keeping flights that would otherwise have been disrupted operating on schedule.
2. Better Fuel Efficiency
Increasing fuel efficiency is one of the key priorities for aerospace companies, as even small improvements in aircraft fuel consumption can have a large impact on a company’s bottom line and emissions. Coupled with 3D printing, the production of lightweight aircraft components is already becoming a reality. In this area, AI-powered systems can help optimize fuel consumption. For example, the French company Safety Line has developed a machine learning tool that can optimize climb profiles to pilots before each flight. Since an aircraft consumes fuel at the highest rate during the climb phase, optimizing this stage can result in considerable fuel savings. French carrier Air Austral has already implemented Safety Line’s solution and expects to save up to 6% of fuel during the climb phase
AI can be used to improve pilot training. AI simulators coupled with virtual reality systems can be used to provide pilots with a more realistic simulation experience. AI-powered simulators can also be used to collect and analyze training data such as biometrics to create personalized training patterns based on the performance of a trainee.
The next important use of AI is assisting pilots during flights. AI-powered solutions inside a cockpit can help to optimize a flight path in real-time by assessing and alerting if needed about the fuel level, systems status, weather conditions, and other crucial parameters. In the future, aircraft could be equipped with smart cameras powered by computer vision algorithms, expanding the visual field of pilots, and thus supporting their safety performance.
4. Innovative product designs
More efficient, lighter parts have always been sought after in the aerospace industry – and artificial intelligence opens new ways of designing them. Generative design is a great example: based on AI algorithms, the new technology encompasses a set of tools and techniques used to create complex product designs from requirements and constraints. Generative design software allows engineers and product designers to explore multiple options in less time to find the best design. This approach is essential in developing new products that integrate more functionality, ultimately making aircrafts lighter and more sustainable. 3D printing is becoming increasingly relevant in leveraging generative design, as it is unconstrained by the limitations of traditional production methods. Hence why major aerospace companies are already investigating the possibilities of this combination, which could be one of the most efficient tools for lowering fuel consumption rates and carbon footprint.
5. Better customer service
Customer satisfaction and performance are particularly important within commercial aviation. AI can be one of the ways for airlines to enhance customer experience and provide better customer service.
There are multiple ways to apply artificial intelligence to provide better customer service. Chatbots are one obvious example: AI-based digital tools that can answer customers’ inquiries in real-time and in a human-like manner. Online chatbots can save time and effort by automating customer support. A survey conducted by SITA has shown chatbots are already used by 14% of airlines and 9% of airports, with 68% of airlines planning to introduce AI-driven chatbots.
However, with more data comes even more opportunities: from travel experiences tailored to individual preferences to customized recommendations and ticket pricing. Forward-thinking airlines are already making moves towards this future. Emirates Vacations, part of Emirates Airline, has recently launched display ads featuring AI-powered chatbots that can give destination and vacation recommendations. Following the 30-day trial campaign, Emirates Vacations has seen an 87% increase in engagement from these chatbot-integrated ads, compared to the standard ads.
While there are many opportunities for AI and machine learning technologies within aerospace, the technology is still very much in its infancy. This can be explained in part by the stringent safety requirements that are vital for such a highly-regulated industry like aerospace. Each new technology introduced to aviation must go through extensive and costly validation/certification processes. And because of the complexity of AI systems, they may not always be able to be certified through traditional FAA processes. This reveals the need to develop new, more efficient verification processes that can help reach AI its full potential in the aerospace industry.
Another challenge is data management. Being the fuel of any AI software, data drives the intelligence of computer algorithms. However, data privacy concerns exist alongside the need to properly manage data of airline customers. It is challenging yet crucial for companies to find ways to implement privacy and cybersecurity practices in the development of AI systems that personal data.
However, as applications for AI in the aerospace industry continue to expand, more airlines are eager to adopt solutions powered by artificial intelligence and machine learning. While AI requires significant investment and still faces some barriers to wider adoption, this innovative technology has enormous potential to optimize manufacturing processes, tackle malfunctions and improve performance.
Credit: IATA, amfg.ai, American Airlines
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