A multi-fidelity digital twin with FMEA fault injection and residual-based classification achieves 96.2% Macro-F1 for 20 engine fault types in general aviation aircraft while providing 4.3x faster inference via GRU surrogate.
Fault detection and isolation of aeroengine combustion chamber based on unscented Kalman filter method fusing artificial neural network
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An Intelligent Fault Diagnosis Method for General Aviation Aircraft Based on Multi-Fidelity Digital Twin and FMEA Knowledge Enhancement
A multi-fidelity digital twin with FMEA fault injection and residual-based classification achieves 96.2% Macro-F1 for 20 engine fault types in general aviation aircraft while providing 4.3x faster inference via GRU surrogate.