Development of practical and verifiable prognostic approaches for gas turbine engine bearings will play a critical role in improving the reliability and availability of legacy and new acquisition aircraft engines. In addition, upgrading current United States Air Force (USAF) engine overhaul metrics based strictly on engine flight hours (EFH) and total accumulated cycles (TAC) with higher fidelity prognostic models will provide an opportunity to prevent failures in engines that operate under unusually harsh conditions, and will help avoid unnecessary maintenance on engines that operate under unusually mild conditions. A comprehensive engine bearing prognostic approach is presented in this paper that utilizes available sensor information on-board the aircraft such as rotor speed, vibration, lube system information and aircraft maneuvers to calculate remaining useful life for the engine bearings. Linking this sensed data with fatigue-based damage accumulation models based on a stochastic version of the Yu-Harris bearing life equations with projected engine operation conditions is implemented to provide the remaining useful life assessment. The combination of health monitoring data and model-based techniques provides a unique and knowledge rich capability that can be utilized throughout the bearing’s entire life, using model-based estimates when no diagnostic indicators are present and using the monitored features such as oil debris and vibration at later stages when failure indications are detectable, thus reducing the uncertainty in model-based predictions. A description and initial implementation of this bearing prognostic approach is illustrated herein, using bearing test stand run-to-failure data and engine test cell data.

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