Situational Awareness for Mission Critical Ship Systems

The US Navy operates a vast fleet of combat and support vessels with complex power control systems under the control and decision authority of human operators. Several current resources such as SPY-1D radar and Vertical Launch System (VLS) and future resources such as railgun, AMDR, and high energy laser (HEL) are energy hungry, exceeding current and planned power generation capability when deployed during dynamic combat operations. As power and energy storage systems grow more complex and the operational requirements during conflicts tax both the ability of the power system to supply resources to disparate systems and the operators to make intelligent sense of the incoming data, machine-intelligence technologies provide an opportunity to ensure operators can make optimal decisions in a timely manner. The IERUS team proposes to develop a robust machine learning based resource management and recommendation system to assist and augment power control system operators. IERUS will employ a proven development strategy enabled by our Machine Learning Toolkit to evaluate numerous ML approaches with little training overhead based on power control system simulation data. The designed algorithm will be tested on realistic reference missions.