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.
Gas path health monitoring for a turbofan engine based on a nonlinear filtering approach
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
citation-role summary
background 1
citation-polarity summary
fields
cs.AI 1years
2026 1verdicts
UNVERDICTED 1roles
background 1polarities
background 1representative citing papers
citing papers explorer
-
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.