CHARLES RIVER ANALYTICS, INC. — Department of Defense SBIR Phase I: N171-052

Description

Because naval operators are responsible for performing preventative maintenance and monitoring the Operational Availability (AO) of a large number of vessel components within complex systems, there is a high risk of unnecessary or unwarranted maintenance of components, which can inflate costs and significantly delay deployments. Although each vessel emits copious amounts of data, this data is confined to isolated storage within each vessel, and is only analyzed when on shore. A system that fuses these disparate data sources to monitor and predict each components AO in real-time can provide a comprehensive picture of fleet-wide (i.e., battlegroup) AO. By generating a prioritized maintenance schedule based on this predicted AO, the system can also significantly reduce the cost of unnecessary maintenance. To address these challenges, we propose to design a System for Naval Data Aggregation and Planning with Probabilistic Reasoning (SNAPPR). SNAPPR uses a mature real-time data aggregation framework that fuses and reasons about data using robust probabilistic models to monitor and predict AO. SNAPPR then uses these predictions to generate an optimized preventative maintenance schedule (PMS) for the naval operator.