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.
Development of robust in-flight prognostics or diagnostics for oil wetted gas turbine engine components will play a critical role in improving aircraft engine reliability and maintainability. Real-time algorithms for predicting and detecting bearing and gear failures are currently being developed in parallel with emerging flight-capable sensor technologies including in-line oil debris/condition monitors, and vibration analysis MEMS. These advanced prognostic/diagnostic algorithms utilize intelligent data fusion architectures to optimally combine sensor data, with probabilistic component models to achieve the best decisions on the overall health of oil-wetted components. By utilizing a combination of health monitoring data and model-based techniques, a comprehensive component prognostic capability can be achieved throughout a components life, using model-based estimates when no diagnostic indicators are present and monitored features such as oil debris and vibration at later stages when failure indications are detectable. Implementation of these oil-wetted component prognostic modules will be illustrated in this paper using bearing and gearbox test stand run-to-failure data.
Results are reported for a project sponsored by the United States Air Force Wright Laboratories entitled “High Temperature Bearing / Lubricant System Development.” The major emphasis of this project was the evaluation of bearing materials with improved corrosion resistance, high hot hardness, and high fracture toughness, intended to meet the requirements of the Integrated High Performance Turbine Engine Technologies (IHPTET) Phase II engine. The project included material property studies on candidate bearing materials and lubricants which formed the selection basis for subscale and full-scale bearing rig verification tests. The carburizing stainless steel alloy Pyrowear 675 demonstrated significant fatigue life, fracture toughness, and corrosion resistance improvements relative to the M50 NiL baseline bearing material. The new Skylube II (MCS-2482) lubricant provided significant thermal degradation improvements with respect to the Skylube 600 (PWA-524, MIL-L-87100) lubricant. Two 130 mm bore Pyrowear 675 hybrid ball bearings with silicon nitride balls were run successfully for 231 hours with Skylube II lubricant at temperatures consistent with IHPTET II requirements.
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