Shifting from data handling to knowledge engineering in aerospace industry

Abstract

The development of increasingly complex systems with improved quality levels becomes more and more challenging. Engineering data frameworks with integrated system models have been developed to manage such systems. This paper presents the experiences that have been made in digital systems engineering in the aerospace domain and focuses on the roadmap that has been taken to establish a knowledge engineering framework. While working with first versions of these tools, it became obvious that an engineering framework reflecting all aspects of an engineering data object was required. In addition, data analytics and technologies used to check data consistency became increasingly relevant. As a consequence, semantically rich data models expressed by ontologies come into focus of forming the engineering framework baseline in conjunction with related technologies such as reasoning, error avoidance based on data analytics, and knowledge-driven engineering environments.