Prognosis/Diagnostics for improved Gas Turbine Engine reliability and maintainability

Impact Technologies, in cooperation with GE Aircraft Engines, Pratt & Whitney and Spectro Inc., propose the development, implementation and validation of diagnostic and prognostic (D&P) technologies specifically designed for real-time, automated healthassessment of oil-wetted components in gas turbine engines. Various corroborating D&P technologies from different engineering disciplines, including oil analysis, vibration analysis and rotordynamic modeling, will be integrated together under this programusing automated reasoning and intelligent data fusion algorithms. With a combination of health monitoring data and model-based techniques, a comprehensive component prognostic capability can be achieved throughout a components life, using model-basedestimates when no diagnostic indicators are present and monitored features such as oil debris and vibration at later stages when failure indications are detectable. Only through the utilization of all of these sources of engineering information canup-to-date assessments and predictions of component remaining useful life be determined for use in automated maintenance and logistics systems.The oil-wetted component prognostic modules will be calibrated and verified using GE F110-129 component failure data, PTO shaft and bearing data from a GE F404 engine and gearbox seeded fault and accelerated failure data. In addition, in-service data fromthe GE F110-129 and Rolls Royce F405 engines will be used to develop and verify the data/knowledge fusion strategies under degraded conditions.The gas turbine engine component prognostic modules will be capable of integrating real-time information from multiple sources on the engine so that critical engine component diagnoses and prognoses can be made more confidently and at an earlier stage.Costly inspection routines and premature component replacements can also be avoided using the prognostic technologies developed under this program. Commercial applications for the prognostic and diagnostic strategies developed can be realized on anymachinery health monitoring application in the electrical generation industries, chemical processing industries, commercial aviation as well as gas transmission and oil industries.