Fault detection and isolation in aircraft gas turbine engines. Part 2: Validation on a simulation test bed

Abstract The first part of this two-part paper, which is a companion paper, has developed a novel concept of fault detection and isolation (FDI) in aircraft gas turbine engines. The FDI algorithms are built upon the statistical pattern recognition method of symbolic dynamic filtering (SDF) that is especially suited for real-time detection and isolation of slowly evolving anomalies in engine components, in addition to abrupt faults. The FDI methodology is based on the analysis of time series data of available sensors and/or analytically derived variables in the gas path dynamics. The current paper, which is the second of two parts, validates the algorithms of FDI, formulated in the first part, on a simulation test bed. The test bed is built upon an integrated model of a generic two-spool turbofan aircraft gas turbine engine including the engine control system.

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