航空学报2025，Vol.46Issue(17)：132-146,15.DOI:10.7527/S1000-6893.2025.31634
基于传感器时序信息增强的剩余寿命预测方法
Remaining useful life prediction method based on temporal information enhancement of sensors
摘要
Abstract
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.关键词
航空发动机/剩余使用寿命/特征注意力/双向长短期记忆网络/残差网络/多头注意力机制Key words
aircraft engine/remaining useful life/feature attention/bidirectional long short-term memory network/residual network/multi-head attention mechanism分类
航空航天引用本文复制引用
曲桂娴,刘冬阳,杨旭,邱天,刘传凯,丁水汀,袁树峥,郭侃..基于传感器时序信息增强的剩余寿命预测方法[J].航空学报,2025,46(17):132-146,15.基金项目
国家自然科学基金(52302510) （52302510）
北京市自然科学基金(3252015) （3252015）
中央高校基本科研费专项资金(YWF-23-Q-1066) （YWF-23-Q-1066）
天目山实验室交叉创新研究团队项目(TK-2024-D-001) （TK-2024-D-001）
北航大学生创新创业训练计划项目(X202410006108) National Natural Science Foundation of China(52302510) （X202410006108）
Beijing Natural Science Foundation(3252015) （3252015）
the Fundamental Research Funds for the Central Universities(YWF-23-Q-1066) （YWF-23-Q-1066）
Tianmushan Laboratory Cross-Innovation Research Team Project(TK-2024-D-001) （TK-2024-D-001）
College Students'Innovative Entrepreneurial Training Program(X202410006108) （X202410006108）
