Fault diagnosis and isolation in aircraft gas turbine engines

This paper formulates and validates a novel methodology for diagnosis and isolation of incipient faults in aircraft gas turbine engines. In addition to abrupt large faults, the proposed method is capable of detecting and isolating slowly evolving anomalies (i.e., deviations from the nominal behavior), based on analysis of time series data observed from the instrumentation in engine components. The fault diagnosis and isolation (FDT) algorithm is based upon Symbolic Dynamic Filtering (SDF) that has been recently reported in literature and relies on the principles of Symbolic Dynamics, Statistical Pattern Recognition and Information Theory. Validation of the concept is presented and a real life software architecture is proposed based on the simulation model of a generic two-spool turbofan engine for diagnosis and isolation of incipient faults.