Rotary Wing Dynamic Component Structural Life Tracking

The aim of this project is to demonstrate the feasibility of a fatigue lifetime tracking system for critical components used in rotary aircraft platforms. By incorporating neural network anomaly detection techniques, algorithms will be developed that track specific critical components and their unique identification across different platforms and rotary aircraft in order to evaluate both new and historic flight load parameter information associated with the operation of these multiple critical components. Combining this information with Health Usage Monitoring System (HUMS) data bases, algorithms will be developed that can both predict anomalous behaviors associated with extended flight load parameters, as well as forecast “just in time” (optimal component retirement life cycles) and outline interim maintenance diagnostic decision points. The fatigue tracking system will further utilize advanced diagnostics and prognostics through the HUMS platform to improve battle readiness of the components and systems, as well as minimize maintenance costs over the life-cycle of the component. Further development will provide an advanced tool to maintenance engineers for determination of necessary diagnostic procedures and schedules for specified components and systems.