Aerospace Xelerated: themes for Cohort 3 (pt. 2)
Smart Maintenance, Adaptive Learning, and Reduced Workload are some of the themes for the upcoming Aerospace Xelerated programme.
We’ve recently launched applications for Aerospace Xelerated, a 3-month funded programme for exceptional autonomy and AI startups to accelerate the growth of the aerospace industry. The programme offers a £100k equity investment per startup from The Boeing Company, support to develop POC opportunities with partners, firsthand access to Boeing, their industry partners, and subject matter experts.
Our next cohort — launching January 2022 —will support startups building AI and Autonomy solutions applicable to aerospace. Our six core themes include Assured Autonomy, Autonomous Navigation, Generative Design, Smart Maintenance, Adaptive Learning, and Reduced Workload.
In pt. 2 of our deep dive into the themes, we will look closer at Smart Maintenance, Adaptive Learning, and Reduced Workload. We spoke to key Boeing stakeholders about the challenges facing the industry and what technology will truly move the needle for aerospace.
In pt. 2, we’re covering the following three topics:
Smart Maintenance — AI applied to predictive analytics on maintenance data to develop smart maintenance solutions.
Adaptive Learning — AI applied to predictive and prescriptive analytics for training of learners based on their role and performance history. Learners have a diverse set of skills, abilities and backgrounds and include pilots and mechanics.
Reduced Workload — Flight automation technologies that reduce crew workload by providing robustness and reliability to automate routine flight tasks.
If you missed pt. 1, head here to learn about Assured Autonomy, Autonomous Navigation and Generative Design.
Smart Maintenance
As we know, machinery is managed by maintenance engineers that evaluate how the machine operates, identify obstacles, and proceed to manually resolve them as and when issues arise. Maintenance engineers often feel rushed in this process, moving quickly between maintaining and running the machine to ensure its smooth operation. Unscheduled maintenance events can have a significant impact on the bottom line.
We spoke to Clinton Thompson, Boeing Research & Technology, about the potential of smart maintenance.
“There is a growing interest in using operational data to optimise maintenance and reduce unscheduled events as well as fine-tune inventory levels to minimise spending without risking operational reliability.”
In an environment where we’re going digital, new solutions can help corporations reduce costs and improve efficiency in the maintenance space. We’re seeing new technologies enable intelligent maintenance operations that have the ability to automatically recognise indicators of failures or defects. This is what is called smart maintenance. It’s about leveraging new technology such as mobile solutions, big data applications and IoT, to ensure that all the equipment required for production operates at 100% efficiency at all times.”
Clinton shed more light on artificial intelligence and machine learning as some of the technologies that will move the needle for the aerospace industry.
“One technology that is showing promise to move the needle is AI/ML. This technology relies on a massive set of data to quickly apply algorithms to train themselves to make predictions. Once the desired performance of the trained algorithm is achieved, they can quickly be deployed to enable better business decisions. This has a high potential for airlines as each aircraft flies multiple flights a day, which generates a good amount of data. Aggregating the data sets from all the aircraft, one then has a massive data set upon which he or she has a probability of success using AI/ML for smart maintenance.”
Smart maintenance includes, but is not limited to, key focus areas such as:
- Predictive maintenance — A form of maintenance that uses data analysis tools and techniques to detect anomalies and possible defects in equipment and processes so you can fix them before they result in failure.
- Prescriptive maintenance — The asset maintenance strategy that uses machine learning to adjust operating conditions for desired outcomes as well as intelligently schedule and plan asset maintenance. Prescriptive maintenance is similar to predictive maintenance but goes beyond simple monitoring, using AI and machine learning to automate the maintenance process.
Predictive and prescriptive maintenance solutions allow airlines to monitor onboard equipment in real-time, detect abnormalities and use analytics to suggest preventative maintenance solutions to reduce and make the most out of airplane on ground (AOG) time.
We spoke to Dr. Amani Alonazi, an Artificial Intelligence Scientist at Boeing, about the safety aspect.
“Safety is a crucially significant element of the aerospace industry. Maintain, Repair and Overhaul (MRO) organizations upkeep and sustain aircraft adhering to strict aviation’s safety standards, and do so around the globe. Smart maintenance technology leveraging artificial intelligence and autonomy will augment the capabilities and situational awareness of aircraft engineers and technicians, enabling major breakthroughs in the way we inspect and maintain aircraft and, upstream, the way in which the next generation of aircraft are designed and manufactured.”
Adaptive Learning
Personalisation is one of the key approaches in efficiently boosting the skills and capabilities of learners. Adaptive learning is a branch of personalised learning. It happens through a computer-based educational system that modifies the presentation of material according to student learning needs. Such systems capture fine-grained data and use learning analytics to enable human tailoring of responses. (link) The ability for students to track their own learning means that they can develop valuable self-monitoring skills, and engage in their personal learning progress.
How is AI applied in this theme? AI applied to predictive and prescriptive analytics for training of learners based on their role and performance history. Learners have a diverse set of skills, abilities and backgrounds. We’re looking for solutions that cater to training and workforce upskilling, from the recruitment office to the factory floor, and include pilots and mechanics.
Reduced Workload
As passenger traffic increases year on year, it’s steadily growing at 3–4% annually, it will increase the occurrence of peak workload events, i.e. when pilots’ attention and actions will be required at the highest level, especially during the approach, landing, turn-around and take off phases. Besides this, pilots could be faced with a combination of unpredictable situations like meteorological conditions and system failures. How can we assist the crew during strenuous activities and help maintain flight safety?
One way this has been approached previously is through speech recognition. The Voice Crew Interaction (VOICI) project aimed to develop an intelligent “natural crew assistant” in a cockpit environment. Flying conditions of high intensity and extreme lulls can lead to higher probability of pilot error. By reducing crew workload, VOICI contributes to optimisation of operations; flight safety, crew awareness, better maintenance, reduced cost of operations and generally higher efficiency and lower stress.
We’re looking for startups building AI and Autonomy solutions with the potential to change the way the aerospace industry operates. We’re excited about the new technologies that can change the way the industry operates today.
Are you a startup founder building a solution in one or more of these themes? Then get in touch. To apply for the upcoming programme, head over to our website.
Learn more about Aerospace Xelerated in our FAQ or watch the recap of our Ask Us Anything webinar. You can also book an Office Hours call to discuss your queries with the programme team.
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