Automated Data Translation for Avionics Health Management through Intelligent Evidence Streams and Databases

摘要

The authors have developed automated data translation and storage to support improved avionics health management through intelligent evidence streams and databases. The architecture permits capture of available evidence in legacy systems to exploit interdependencies and relationships of multiple disparate line replaceable units (LRUs) to provide embedded onboard and improved at wing reasoning. This can dramatically increase the effectiveness of current repair and maintenance efforts through a reduction in ambiguity and subsequent decrease in the high percentages of cannot verify (CNV) test results that plague the present system. Evidence and context preservation, demonstrated through intelligent data stream and database design, supports continuous learning and system evolution through technology changes and system upgrades. These developments support an avionics health management system that maintains information continuity from onboard and at-wing to the operational and logistic chain. Higher levels of reasoning and data exploitation at each point offer continued assessment, improvement, and evolution of the system.