DRAS (Data Refinery for Aviation Sustainment)

Noise in the source data limits effectiveness and utility of models, algorithms and tools used by the Army for Condition Based Maintenance (CBM+) of its aviation fleet. This impedes the sustainment and availability of its aviation fleet. The Data Refinery for Aviation Sustainment (DRAS) applies methods and techniques from statistics, data science, machine learning and data management to refine the source data prior to its integration into algorithms and models. DRAS characterizes the causes and types of noise in the source data, and then applies the appropriate methods to effectively filter this noise from the data while maintaining the integrity of the data itself. In this Phase 2 project ATCorp will refine the system architecture and design of software components from Phase 1 and demonstrate data cleaning methodology using actual US Army example data sources. The ultimate goal of this Phase 2 work is to develop a complete TRL6 software prototype of DRAS and assess its impact on the performance of US Army aviation sustainment tools.