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The DoD has various vehicle platforms powered by high performance gas turbine engines that would benefit greatly from predictive health management technologies that can detect, isolate and assess remaining useful life of critical line replaceable units (LRUs) or subsystems. In order to meet these needs for next generation engines, dedicated prognostic algorithms must be developed that are capable of operating in an autonomous and real-time engine health management system software architecture that is distributed in nature. This envisioned prognostic and health management system should allow engine-level reasoners to have visibility and insight into the results of local diagnostic and prognostic technologies implemented down at the LRU and subsystem levels. To accomplish this effectively requires an integrated suite of prognostic technologies that can be applied to critical engine systems and can capture fault/failure mode propagation and interactions that occur in these systems, all the way up through the engine and eventually vehicle level. In the paper, the authors will present a generic set of selected prognostic algorithm approaches that can be applied to gas turbine engines, as well as provide an overview of the required reasoning architecture needed to integrate the prognostic information across the engine.

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AbstractThis paper provides an update to [1] on the developments associated with a Prognostics and Health Management (PHM) system design tool that integrates a model-based FMECA methodology with state-of-the-art system simulation directly linked to downstream Life Cycle Costs (LCC). This design tool will seek out recommended PHM system designs based on a cost function that accurately represents key LCC variables such as system availability, maintainability, reliability, and failure mode observability. The tool will be capable of assessing PHM sensor requirement specifications at the component and subsystem levels, and will then allow for integration into a broader integrated system model. Tradeoff, sensitivity and "what if" analysis will then allow the designer/user to examine the cost/benefit relationship of either adding or removing sensor and algorithms under consideration for the PHM design. An interactive database of existing PHM technologies for specific applications will also be accessible within the design tool for suggesting sensors/algorithms for monitoring various system parameters.Finally, the approach introduces a collaborative, web-enabled environment for enhanced realization and virtual simulation of PHM system design. A simplified example of a Health Management system cost/benefit analysis on an aircraft electromechanical valve is provided for illustration of the concepts introduced.