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This research paper presents an enhanced digital twin-driven fault detection and isolation (FDI) method for gas turbine engines, integrating a digital engine model, sensor series imaging, and a convolutional neural network (CNN). The proposed method significantly improves diagnostic accuracy from 68.25% to 97.95% and fault isolation rate from 85.91% to 99.26%, while reducing false alarm rates. The effectiveness of the method is validated through actual ground test data and comparative simulations, demonstra

Author

Mia Lima

Language

EN