Application of a Bank of Kalman Filters for Aircraft Engine Fault Diagnostics

ABSTRACT In this paper, a bank of Kalman filters is applied to aircraft gas turbine engine sensor and actuator fault detection and isolation (FDI) in conjunction with the detection of component faults. This approach uses multiple Kalman filters, each of which is designed for detecting a specific sensor or actuator fault. In the event that a fault does occur, all filters except the one using the correct hypothesis will produce large estimation errors, thereby isolating the specific fault. In the meantime, a set of parameters that indicate engine component performance is estimated for the detection of abrupt degradation. The proposed FDI approach is applied to a nonlinear engine simulation at nominal and aged conditions, and the evaluation results for various engine faults at cruise operating conditions are given. The ability of the proposed approach to reliably detect and isolate sensor and actuator faults is demonstrated. NOMENCLATURE A16 Variable bypass duct area A8 Nozzle area BST Booster CLM Component Level Model FAN Fan FDI Fault detection and isolation FOD Foreign object damage HPC High-pressure compressor HPT High-pressure turbine LPT Low-pressure turbine P27 HPC inlet pressure PS15 Bypass duct static pressure PS3 Combustor inlet static pressure PS56 LPT exit static pressure T27D Booster inlet temperature T56 LPT exit temperature TMPC Burner exit heat soak WF36 Fuel flow XN2 Low-pressure spool speed, measured XN25 High-pressure spool speed, measured XNH High-pressure spool speed, state variable XNL Low-pressure spool speed, state variable

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