LSTM-Based Performance Prediction of Reciprocating Seals for Aircraft High-Pressure Actuators

The reciprocating dynamic sealing system is crucial for the operational reliability of aircraft high-pressure hydraulic actuators, particularly for civil aircraft with 21 MPa (e.g., B737, A320) and 35 MPa (e.g., B787, A380) pressure systems, where real-time monitoring and prediction of friction and oil-film thickness are essential for health assessment. This study proposes a novel data-driven predictive framework for dynamic seal performance utilizing a long short-term memory (LSTM) network. A specialized reciprocating seal test rig was developed to acquire high-fidelity multimodal data. By integrating ultrasonic detection, fiber Bragg grating (FBG) sensors, and load cells, the simultaneous measurement of oil-film thickness, contact stress, and friction force under high-pressure conditions up to 35 MPa was achieved. The measured oil-film thickness, determined by the sealing gap, provides the physical basis for leakage characterization. An LSTM-based model was then trained on this time-series dataset to predict oil-film thickness and friction force. The results show that the LSTM model achieves a coefficient of determination (R2) above 0.94 and a symmetric mean absolute percentage error (sMAPE) below 2% for both predictions on the test set. Ultimately, this research provides theoretical support for the condition assessment and leakage risk early warning of dynamic seals, advancing the application of artificial intelligence and data-driven methodologies within aviation sealing technologies.