Remaining useful life prediction method based on temporal information enhancement of sensors

To address the challenge of accurately predicting the Remaining Useful Life （RUL） of aircraft engines on⁃ line, this paper pro-poses a novel RUL prediction method that enhances multi-source sensor temporal feature informa⁃ tion. The approach first establishes a prediction network framework by integrating the self-attention mechanism with Bi⁃ directional Long Short-Term Memory （Bi-LSTM） networks. This framework captures the long-term temporal depen⁃ dencies of multi-source sensor signals and the coupling relationships between their time-varying performance, enabling the extraction of temporal features that in-fluence RUL. To address the potential gradient vanishing issue during train⁃ ing, a residual module is introduced, improving model stability. Additionally, a multi-head self-attention mechanism is employed to extract and enhance key features, leading to dual improvements in both the accuracy and stability of RUL online prediction. Comparative experiments using NASA’s C-MAPSS aircraft engine dataset demonstrate the effec⁃ tiveness of the proposed method. The results show that the method leverages sensor temporal information to make precise RUL predictions and degradation trend forecasts across a wide range of time and spatial scales. Specifically, the Root Mean Square Error （RMSE） of RUL prediction is reduced by an average of 21. 74% compared to other deep learning models, while the coefficient of determination （R2） is improved by an average of 15. 81%. This approach of⁃ fers valuable technical support for the development of aircraft engine health management systems and predictive main ⁃ tenance strategies.