Abstract: To enable real-time performance monitoring and degradation assessment of aircraft engines, a digital twin-based methodology for real-time evaluation of engine performance degradation is proposed. A performance digital twin architecture was designed and implemented by integrating Long Short-Term Memory (LSTM) recurrent neural networks with the engine’s physical structural configuration. Base-line models of the performance digital twin were established using flight parameter data from the initial operational flights of an engine. The model demonstrates high-fidelity simulation capabilities for replicating the engine’s performance across diverse flight conditions. By feeding real-time operational parameters and flight state data into the baseline model, the real-time performance metrics of a pristine (non-degraded) engine under current operating conditions are simulated. Comparative analysis between simulated outputs and actual sensor measurements enables quantitative assessment of the engine’s instantaneous performance degradation. A case study involving 185 flight cycles validated the framework: Baseline models constructed from the first three flights achieved mean absolute relative errors below 0.98%, 0.94%, and 1.89% for rotational speed, pressure, and temperature predictions, respectively, with single-point inference times under 0.14 milliseconds, confirming the reliability of real-time digital twinning. The degradation assessment results of this method align well with traditional methods, demonstrating significant feasibility and advantages.

Key words: Aero-engine, Digital Twin, Performance Monitoring, Degradation Assessment, LSTM, Architecture-Driven