The Impact of Industrial IoT and AI in Aerospace

In the past years, manufacturing industries shifted their focus to smart manufacturing for all the right reasons. A few benefits of implementing smart manufacturing include quick adaptation to meet the requirement of the dynamic market and improvement in the supply and value chain. In aerospace, regarding smart manufacturing, industrial IoT and AI play a crucial role in bringing disruptive changes. Here is how industrial IoT and AI are changing the aerospace industry.

Efficient data life cycle

The data life cycle in aerospace is long because it houses many components, including engineering, manufacturing and after-marketing. In addition, one must monitor the design process, system health, and digital supply chain. A combination of everything brings in tons of data for the aerospace industry, and AI can help the industry to use this data efficiently.

With improved connectivity in the airplane, as visible from the availability of UnitedWifi, which even allows the flyers to connect to the internet on the go, it is possible to integrate all the systems. Ultimately, it helps in customer, maintainability, fleet, and revenue management.

The data collection and analysis help improve aspects like performance, fabrication, and design. Also, when this data is shared across different stakeholders and other partners in the ecosystem, it allows businesses to engage in innovative transformation.

Improved flow of data

The Industrial Internet of Things, or IIoT, is used for advanced technology in the aerospace industry. It helps bind all the aerospace networks, which makes communication easier and further improves the flow of data. IIoT, through the connected devices and sensors, helps generate data to bring insightful information.

For example, the aerospace industry can use IIoT in predictive maintenance to monitor speed, temperature, and amperage draw performance indicators. Additionally, IIoT allows companies to engage in better resource allocation and optimization of workers. It can also play a critical role in the dynamic routing algorithms by combining RFID-tracked inventory and live machine status.

Diagnosis of problems

Along with AI, Machine Learning (ML) is increasingly used in the aerospace industry. In fact, it has become an integral part of the manufacturing process with increasing data load from advanced and connected manufacturing systems. It also helps the analysts better to understand air traffic manners and further control it.

Furthermore, AI and ML can help diagnose potential problems of aircraft in flight. Consequently, it is easier to repair and fix the issue efficiently. Other ways in which the combined role of AI and ML comes into play include making the aircraft more energy efficient and greener. Also, AI and ML can quickly start working with just an internet connection, for example, in British Airways, which offers in-flight Wi-Fi.

Low labor cost and increased productivity

AI, which can be used for automation, has enormous potential in the aerospace industry. Autonomous robots can help lower labor costs and increase productivity in the aerospace industry, like any other sector. However, it comes with the challenge of high expense, but it is possible to manage it by using the same for multiple purposes.

Increased fuel efficiency

Fuel quality highly matters for aerospace companies. Reducing fuel consumption even by a tiny margin can increase profitability with a high margin. For example, it is possible to use AI-powered systems and machine learning tools to optimize flight climb profiles, which cut fuel consumption by 5-7%. This analysis is critical since an airplane’s climbing process consumes the most fuel.

Pilot training

Before flying an actual airplane, the pilots undergo training in different facilities. When used in training facilities, AI creates apt simulation environments for the pilot. The perfect combination for such a simulation environment includes AI and virtual reality. Also, the result of this training can further be used to create a personalized training experience for the pilot to help improve their weak points.

Enhanced customer experience

It is essential to improve customer experience, especially for commercial aviation. AI contributes highly to this segment by improving customer engagement and providing the support they require. The most used example of AI in customer support includes chatbots which solve the inquiries of the customers in real-time in a human fashion.

Experts believe that the aviation industry will undergo a massive digital transformation in the next five years. However, to meet the maximum potential, it is crucial to leverage all the current technological opportunities and harness data-driven insights.