AN INTEGRATED BEARING PROGNOSIS APPROACH USING ADVANCED HEALTH MONITORING AND MATERIAL-LEVEL MODELING

摘要

A comprehensive gas turbine engine bearing prognosis approach is presented that fuses available vibration and lube system information with model-based predictions to calculate remaining useful life. Specifically, correlation between sensed data and fatigue-based damage accumulation models using a stochastic Yu-Harris bearing life model was developed to provide the remaining useful life assessments. 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. Thus, using model-based estimates when no diagnostic indications are present and using the monitored features such as oil debris and vibration fault features at later stages when failure indications are detectable. This integration is also used to help reduce the uncertainty in model-based predictions. A description and initial implementation of this bearing prognosis approach is illustrated herein, using bearing transitional failure data and engine test cell data for validation.