Data Analytics Tools for the Automated Logistics Environment (ALE)

Introducing advanced analytics to aviation sustainment improves fleet readiness by aiding problem diagnosis and predictive maintenance. Mosaic’s Automated Logistics Environment Analytics Application (ALE AA) enables the use of high-end predictive analytics by analyzing data collected from aircraft in-flight performance monitoring systems to provide insight into fleet-wide aircraft performance. Enhancing the Hawkeye Flight E-2D Integrated Analytics (Hawkeye Flight) tool with advanced data science models and an intuitive user interface will provide improved aircraft readiness, component failure predictions, and maintenance diagnostics and will allow a refinement of component inventory policies. In Phase I, Mosaic and subcontractor, KBR Inc., focused on a preliminary design to increase mission capability rates by deploying machine learning (ML) tools within the Hawkeye Flight family of services. ALE AA leverages KBR’s expertise with Hawkeye Planeside / Hawkeye Web software and Mosaic data science expertise to deploy state-of-the-art ML algorithms using the large volume of data found in E-2D flight packets. Mosaic’s software mined hundreds of thousands of lines of advisories, cautions, warnings (ACAWS) data and built-in-test (BIT) failures to provide extremely useful descriptive analytic visualizations. The flight packets provided sufficient information for our data scientists to perform descriptive and diagnostic analysis and model failure conditions and relationships using recurrent neural networks (RNN) and convolutional neural networks (CNN) to begin very powerful predictive analytics using nothing but ACAWS / BIT data. Work in Phase II will continue the design and development of ALE AA and integrate and deploy it within the Hawkeye Flight E-2D Integrated Analytics platform. Work will transform from concept refinement to a technology development, system development & demonstration, and production & deployment multi-stage setting. Mosaic will use an agile software development methodology with a focus on rapidly deploying vital capabilities to war fighters. In the Phase II Option year, the development will seek to incorporate additional Navy maintenance software systems such as the Naval Aviation Logistics Command Operating Maintenance Information System to achieve diagnostic and prescriptive analytics to facilitate true performance-based maintenance. Our overall objective in Phase II is to advance development of the ALE AA system to ensure it is ready for more immediate fleet deployment. We intend to accomplish this by improving the data analytics capabilities developed in Phase I, expand the scope of data used by the analytical models, and to become fully integrated into the Hawkeye Flight acquisition process. Algorithms and software performance will be tested in KBR’s “in-house” test environment in Base Year 1 and in the government “7.2” test environment in Base Year 2. Final transition to a live Navy system will occur in the Option Period.