Prognosis of faults in gas turbine engines

A problem of interest to aircraft engine maintainers is the automatic detection, classification, and prediction (or prognosis) of potential critical component failures in gas turbine engines. Automatic monitoring offers the promise of substantially reducing the cost of repair and replacement of defective parts, and may even result in saving lives. Current processing for prognostic health monitoring (PHM) uses relatively simple metrics or features and rules to measure and characterize changes in sensor data. An alternative solution is to use neural nets coupled with appropriate feature extractors. We have developed techniques that couple neural nets with automated rule extractors to form systems that have: good statistical performance; easy system explanation and validation; potential new data insights and new rule discovery, novelty detection; and real-time performance. We apply these techniques to data sets data collected from operating engines. Prognostic examples using the integrated system are shown and compared with current PHM system performance. Rules for performing the prognostics will be developed and the rule performance compared.