DELPHI: Decision Engine and Learned Prognostics Human-machine Interfaces for Shipboard Maintenance

For Condition Based Maintenance Plus (CBM+) and Prognostics and Health Management (PHM) programs to help the Strike Group (SG) Operational availability (Ao) and logistics efficiency, the Navy needs an effective decision support system (DSS). Visualization of the PHM data will enable the decision makers to easily interpret the results of the DSS and make decisions that are more consistent with maximizing Ao. An efficient data visualization tool should support interactive exploration/understanding of DSS recommendations and alternatives. A well-designed visualization system will adapt to the requirements of the user, expose patterns, trends, correlations, and provide multi-tiered results to support advanced decision-making. Intelligent Automation, Inc., and its collaborators propose to develop DELPHI, a Decision Support Engine and Learned Prognostics Human-machine Interfaces for Shipboard Maintenance. The key innovation of this proposal is to develop a data fusion center comprising of the following four components (1) Visual simulation based modeling tool (what-if scenarios); and (2) Stochastic optimization engine (to compute optimal policies) (3) Visualization engine (to optimally present context-aware information);and (4) Advanced visualization interfaces (responsive software that adapts to different interfaces). The first two push the state-of-the-art in decision support capabilities, whereas the other two improve the visualization.