Predictive Analytics for Sustainment and Reliability of Missile Fleet (PASAR-MF)

This SBIR project will develop and demonstrate analytics capability and analytics architecture for sustainment of a ground-based intercontinental ballistic missile (ICBM) weapon system. The capability will be provided as a stack of analytics functions that collect and fuse maintenance, test, inspection, and engineering data from multiple data streams, formats, and sources. The analytics in the stack will identify most urgent sustainment needs based on sensor data and results of non-destructive tests. They will provide lifetime prediction based on ambient condition exposure history and using aging models that implement physical principles and physics-based understanding. They will further predict fleet reliability and maintainability issues such as entering the wear-out phase for subsystems. Finally, the analytics will define the optimal resources on most pressing sustainment issues, such as reducing costly premature and unnecessary component replacement or maintenance. The analytics will be applicable to most ICBM subsystems. The capability development in this project will build upon knowledge and experience accumulated in aviation sustainment and in the emerging Industrial Internet of Things (IIoT) domain. Analytics approaches from these domains will be adapted to ICBM sustainment.