Gas turbine engine diagnostic systems often utilize data trending and anomaly detection to provide a measure of system health. These systems provide significant benefits for trending shifts in engine performance and diagnosing system degradation that requires some maintenance action. However, this approach may be limited in the ability to uniquely identify damage for select components and failure modes. Advanced prognostic systems are being developed to work symbiotically with state of the art diagnostic techniques in use today; these advanced systems use advanced material and component damage evolution modelling linked with system-level structural analyses to intelligently guide the health management system to search for specific signatures that would be expected from key changes in component and system health [1,2,3,4]. Material damage models, advanced component models, and novel system-level structural analyses are being used to generate newly defined “structural transfer functions” (STFs) that provide a link between sensed parameters and the remaining capability of specific components, and the system. The characteristic damage signatures vary by component type and failure mode, and hence the specific STF approach varies among component types. An initial STF approach was developed and demonstrated for a specific component and damage type [5] under an initial feasibility program. This STF-based prognosis approach is fundamentally different from the traditional modal analysis based NDE approach used for crack detection. This presentation will review this novel STF-based prognosis approach, and consider examples of STFs characteristic of specific components and damage types, as well as progress towards the development of tools that are enabling system-level STF development [6].

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