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Machine Learning (ML) applications in the Aerospace and Defense industry

An article by Aggelos Spyratos, Sn. ML Engineer at SOTIRIA Technology


Today, there are many discussions about how aerospace and defense organizations can take advantage of the technological progress in machine learning, deep learning, and artificial intelligence (AI) that has already been largely deployed in other industries. The main reason for this trend is that they can heavily contribute to the creation of innovative applications that help commercial, military and governmental stakeholders better identify threats, locate and resolve equipment issues before failures occur, and detect unseen risks.





What is AI and machine learning?


AI, like so many technology buzzwords, can mean different things to different people. For us, at SOTIRIA Technology, an AI system is one that leverages software functions created through a machine learning process rather than through traditional programming. Data, rather than source code, is the critical element. The performance of an AI application is shaped by the data used to train the application.


Without going into detail on machine learning algorithms or approaches, which is beyond the scope of this article, we can generalise that the power in machine learning is rooted in its ability to model complex systems and environments far beyond what we can reasonably build in traditional software.


Why is it crucial for the aerospace and defense industry?


Aerospace and defense organizations utilize a great variety of systems and measuring equipment for applications such as Intelligence, Surveillance and Reconnaissance (ISR), Geolocation, Attitude Control, Assets security etc. The use of such tools generates a remarkable amount of data for each organization every year, thus providing a treasury that can be unlocked via machine learning and data analysis applications in order to solve operational problems on the field. A good example of such an application, that will be further analysed in a separate article, is that of a condition monitoring and preventive maintenance system, carefully designed to help organization's engineering teams reduce the maintenance costs for critical equipment and make their day to day work more efficient.

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