Gas turbines are widely used, highly critical, components for generating electrical power and propulsion on modern US Navy ships. Increasing numbers of sensors and sophisticated health management systems are being integrated into shipboard systems to enhance monitoring, performance, diagnostic, and maintenance planning capabilities. Development of a decision support tool to fuse multiple independent system health indicators provides the US military with a readily deployable technology for enabling comprehensive assessment of overall system platform health and mission readiness. This technology leverages existing open source data systems and on-board diagnostic and prognostic modules to efficiently and seamlessly employ advanced reasoning and self-learning techniques to predict high level system health and readiness from component level inputs. This paper summarizes the work associated with development of a software application to provide real-time mission readiness assessment for US Navy ships. The application incorporates several novel approaches including use of a uniform gray-scale method for identifying system health and readiness; fusion of multiple independent low-level indicators to predict overall system health and readiness; methodologies to account for the interactive effects of interconnected subsystems on overall system health and readiness; and use of self learning techniques to provide continuous refinement to future system health and readiness predictions.

This content is only available via PDF.
You do not currently have access to this content.