Situational Awareness for Mission Critical Ship Systems

Battle situations can create loss of essential ship functions and capacities such as communications, power, cooling, weapons, etc. System state restoration today is limited by state constraints imposed by the extremis conditions, resource availability, access to critical system information, and by operator proficiency and experience. The explosion of data from various interdependent sensors, each competing for the operators attention through numerous information displays and alerts/alarms can result in cognitive overload. The Navy recognizes that a different approach is needed to make sense of the situation, and present timely, contextually-coherent, actionable, and goal-oriented options to the operator. We propose research into the selection and fine-tuning of machine learning algorithms to develop a system state awareness for mission-critical Navy ship systems. We propose to add the machine learning engine and meta information generation algorithm into the Navys Condition Monitoring System (MCS) baseline software. The ML engine is trained on both machinery and other sensor data as well as historical event data. We also propose use of synthetic event data where possible to expand/enhance training data sets.