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.
Probing a novel machine tool fault reasoning and maintenance service recommendation approach through data-knowledge empowered LLMs integrated with AR-assisted maintenance guidance
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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.