Yang Yixu, Su Xiaoying, Wang Chaoyong, Liu Hongxi, Fu Kunhao, Xie Tao, Zhang Zishuo
School of Jilin Emergency Management, Changchun Institute of Technology, Changchun, 130021, China.
School of Electrical Engineering and Information Technology, Changchun Institute of Technology, Changchun, 130021, China.
Sci Rep. 2025 Jul 29;15(1):27696. doi: 10.1038/s41598-025-13387-4.
Remaining useful life (RUL) prediction of aircraft engines is of great significance for the safety and reliability of aircraft operations. However, the high feature dimension and noise of the raw data cause difficulties for existing methods in extracting long sequence time features and allocating weights. In this study, we propose a RUL prediction network named BLTTNet with enhanced feature extraction ability to address these difficulties. We utilize the efficient implicit feature extraction capability of BiLSTM to represent high-dimensional features. Then, DCEFormer and TCN are used to process the global and local information of the time series respectively. Specifically, DCEFormer with a Transformer structure enhances the allocation of feature weights by processing the contributions of different features in both the time step dimension and the sensor dimension, thereby improving the accuracy of RUL prediction for mechanical equipment. Meanwhile, an adaptive fusion method is employed to fuse the model. Finally, we conducted experiments on CMAPSS dataset. On the FD002 subset, it achieved excellent RMSE and score performance, with average improvements of 5.91 and 13.23% respectively.
航空发动机剩余使用寿命（RUL）预测对于飞机运行的安全性和可靠性具有重要意义。然而，原始数据的高特征维度和噪声给现有方法提取长序列时间特征和分配权重带来了困难。在本研究中，我们提出了一种名为BLTTNet的RUL预测网络，以增强特征提取能力来解决这些困难。我们利用双向长短期记忆网络（BiLSTM）高效的隐式特征提取能力来表示高维特征。然后，分别使用深度卷积增强型Transformer（DCEFormer）和时间卷积网络（TCN）来处理时间序列的全局和局部信息。具体而言，具有Transformer结构的DCEFormer通过处理时间步长维度和传感器维度中不同特征的贡献来增强特征权重的分配，从而提高机械设备RUL预测的准确性。同时，采用自适应融合方法对模型进行融合。最后，我们在CMAPSS数据集上进行了实验。在FD002子集中，它取得了优异的均方根误差（RMSE）和评分性能，平均提升分别为5.91%和13.23%。
