Software, networks and data

The digital transformation of the aeronautics and space industry is happening at BNAE through the development of European standards or General Recommendations (RG Aéro) by experts from the Software, Networks and Data Discipline (D-LRD), making it possible to clarify practices, define and frame innovative technologies, define quality and security levels for products and services, or facilitate compliance with regulations.

CN RFID

CN RFID

The RFID CN covers normative documents dealing with RFID technology, both passive and active, extended to the “Contact Memory Button”, and explores the standardization of requirements specifications, test methods, success criteria, and other aspects related to the use of RFID (such as the attachment of tags to supports, etc.) in the aeronautics and space sector.

CN ALT - Long Term Archiving

CN ALT - Long Term Archiving

The CN ALT covers the specification of auditable processes for long-term archiving of data for aeronautical products (archiving requirements, methods, scenarios, process descriptions, recommended practices, etc.).

CN PHM - System Health Monitoring

CN PHM - System Health Monitoring

The CN PHM covers integrated test methods that take into account the system architecture, and defines requirements for the reliability, implementation, access and reporting of BITs. It also covers the enterprise architecture of centralized management of the system health record, by studying the data path in the technical maintenance organization, the description of users (functions, services provided), and by giving recommendations regarding new services and the control of these services in a context of connected objects and organizations in the aeronautics and space sector.

CN VAML - Validation of machine learning algorithms

CN VAML - Validation of machine learning algorithms

The CN VAML covers best practices in the field of validating machine learning algorithms adapted to the aeronautical world. It focuses on describing a number of use cases and defining good practices for developing critical embedded AI-ML components. The classes of AI-ML algorithms covered are supervised learning, reinforcement learning, and unsupervised learning.

CN JUMN - Digital Twin

CN JUMN - Digital Twin

The JUMN CN aims to characterize Digital Twins, to facilitate and streamline their emergence and cooperation (digital twin systems) in the aeronautics and space sector. The CN covers in particular the issues of interoperability and variability of interconnections.

CN ECOA - Component-Oriented Architecture

CN ECOA - Component-Oriented Architecture

The ECOA CN covers real-time application software embedded in aeronautical applications, offering a component-to-service model where the implementation of OS APIs is hidden from developers by the principle of containers (i.e. separation of technical code and application code).

CN MODL - Models and simulations

CN MODL - Models and simulations

The CN MODL addresses the normative aspects for achieving good credibility and reliability of behavior simulation studies (mechanical, thermal, aerodynamic, thermodynamic, electromagnetic, propulsion, trajectories, etc.). The topics concerned include the validation of the software used, the management of simulation processes (including validation of simulations), the control and traceability of input data and results, and the management of operator skills.

CN HUMS - Health and use of equipment Monitoring

CN HUMS - Health and use of equipment Monitoring

The CN HUMS covers the standardization of exchanges for “low level” monitoring of the health and use of equipment (HUMS), its issues and opportunities, particularly in terms of interoperability at the level of frames exchanged by HUMS devices.