Omina Technologies > Aerospace
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Creating digital twin technologies for aerospace applications that provide valuable insights, optimize processes, and enhance decision-making is challenging

It is crucial that the aerospace industry:

Improve the safety and training of employees : there is an aging workforce in the space industry and bringing in a new generation of talent is imperative.

Innovate, prototype, and test quicker and more efficiently : Space is getting crowded. There is increased competition between traditional players in the global space ecosystem and new private industry players. Speed to market is very important, now more than ever.

Improve decision making in mission critical situations : better decisions based on data that can be trusted is key to success in a crowded and increasingly competitive global space industry. 

Reduce downtime and costly equipment failures : every minute of downtime due to equipment failure in the space industry is extremely costly and can jeopardize reputations.

Optimize processes across the ecosystem: The space industry is often seen as a cost center and funding remains a key challenge so identifying inefficiencies and improving them is vitally important. 

More and more, the space industry is turning to digital twin technologies to provide a digital model of physical assets, processes, or systems. 

Digital twin technologies simulate real world entities by creating a virtual twin of real-life assets (e.g. aircraft’s, satellites, etc.) processes or systems. This virtual twin enables to reproduce complex situations for better predictions, easier monitoring, optimization, and more accurate data analysis. Digital twin technologies has enabled a more proactive approach towards key problems in the space industry.

Creating a digital twin in space poses unique challenges

Digital twin in aerospace face data privacy, data protection, security and interoperability challenges.
Regular AI cannot take on these challenges.
Trustworthy AI can enable trustworthy digital twin that ensure:
– robust data privacy practices and strong data protection safeguarding personal and/or sensitive data.
– secure APIs enabling seamless and cheaper data exchange

Trustworthy, compliant, privacy preserving, security minded artificial intelligence is the key to implementing successful digital twin technologies. 

The role of digital twin is a revolutionary shift in the aerospace industry and the use of digital twin is expected to grow enormously. However, enabling digital twin technologies with artificial intelligence is complex. It involves enormous amounts of data from a multitude of systems and stakeholders. The data is often of a highly sensitive nature and interconnected. Protecting the privacy of sensitive data and ensuring that legal compliance is adhered to is crucial to successful digital twin technology initiatives. 

Digital twin technologies – comprised of data-rich models and machine learning – allow applications to gain an accurate representation of complex cyber-physical models. However, the implicit need for resilient data protection must be achieved by integrating privacy-preserving mechanisms into the DT system design as part of an effective defence-in-depth strategy.

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Alongside the value gained from utilising Digital Twin technology, developing intelligence capabilities based on Digital Twin bring multi-layered risk to data privacy.

The threat landscape affecting data privacy ranges from eavesdropping to data destruction. Also, it includes opportunities for resourceful attack actors such as Advanced Persistent Threats (APT) to leverage AI-powered methods to identify new attack vectors as they emerge.

This  AI approach  maliciously  exploits the continuous transformation of Digital Twin (a  Digital Twin  is a live model) diminishing the effectiveness of conventional cyber defences.

One of Digital Twin building blocks is data derived from sensors embedded  in  the physical object.  Data, including sensors-generated data,  is multifaceted and can be considered sensitive for reasons beyond personal privacy.

Data can be commercially or financially sensitive, be it a matter of national security, health-related or Intellectual Property (IP). Therefore, defense mechanisms need to evolve to effectively protect intellectual property during the entire lifecycle of the Digital Twin data pipeline.

Use Cases

Trustworthy AI can be used to create trustworthy digital twin for:
– digital prototyping and design optimization reducing the cost of innovation and increasing the speed to market
– predictive maintenance to reduce down time and maintenance costs
– immersive training to increase safety
– improved supply chain management 

Why Omina Technologies?

Omina Technologies has been an expert in ethical and trustworthy AI since 2016. We enable you to become a leader in aerospace by supporting you in building trustworthy AI solutions by design: