Nebula Public Library

The space industry and allied agencies have long acknowledged the interest and added value of Model-Based System Engineering in expediting the design, development, deployment, and verification of space systems. The definition of models and equipment that can serve the diverse objectives of the system engineering process has been the focus of numerous initiatives over the past few decades. The next stage is to employ MBSE broadly throughout the system life cycle across disciplines and the supply chain.

Main achievement: Development of the first blockchain ecosystem for space system engineering, as a global configuration tool that can model the business logic of concurrent design. Addressing the MBSE challenges of  controlling data exchange and traceability across system development and operations lifecycle.

Results: Two use cases have been successfully implemented

The Integrated MBSE Analytics Platform (IMAP) has been developed in the course of the OSIP [OSIP] MBSE campaign’s MBSE2DataLake project. It demonstrates how MBSE data can be downstreamed from early phases and integrated with Telemetry data or system operations data. Applying MachineLearning analytics and customized visualization on these sets of integrated data provides seamless analysis capabilities and data browsing experience for the user. The approach leads to linking the former separated domains of System Design and AIT/Operations.

Tables and graphs are knowingly used to organise data within a company with different levels of depth and complexity. Knowledge graphs (KGs) are particularly useful because they can cope with data diversity (high-quality complete data and sparse and incomplete data), they have a high degree of scalability and flexibility (the semantic data model can be inter-operational, large, wide and as deep as needed) and, last but not least, they provide reasoning and inference capabilities.