基于多特征融合的航空发动机剩余寿命预测

针对航空发动机剩余可用寿命(RUL)预测任务中代表性特征提取不充分导致RUL预测精度较低等问题, 提出了一种基于多特征融合的航空发动机RUL预测方法. 利用指数平滑法(ES)降低原始数据中的噪声干扰, 得到相对平稳的特征数据. 使用双向长短期记忆网络(Bi-LSTM)提取特征数据的时序特征, 利用多头注意力机制(Multi-attention)为时序特征赋予权重; 设计卷积长短期记忆网络(Conv-LSTM)提取特征数据的时空特征; 提取特征数据的手工特征并使用Softmax函数计算权重. 设计一个特征融合框架将上述特征进行融合, 然后通过全连接网络回归实现最终RUL预测. 使用C-MAPSS数据集对模型进行仿真验证, 与Bi-LSTM等模型进行对比, 模型RUL预测精度更高, 适应性更好.

Remaining Useful Life Prediction of Aeroengine Based on Multi-feature Fusion

To solve the problems of low prediction accuracy in aeroengine remaining useful life (RUL) prediction due to insufficient representative feature extraction, this study proposes an RUL prediction method based on multi-feature fusion for aeroengines. Exponential smoothing (ES) is performed to reduce the interference noise in the original data and thereby obtain relatively stable feature data. The time series features of the feature data are extracted by the bidirectional long short-term memory (Bi-LSTM) network and then assigned weights through the multi-head attention mechanism (Multi-attention). A convolutional long short-term memory (Conv-LSTM) network is designed to extract the spatio-temporal features of the feature data. Then, the handcrafted features of the feature data are extracted, and weights are calculated from the Softmax functions. A feature fusion framework is designed to fuse the above features, and RUL prediction is finally achieved by fully connected network regression. The commercial modular aero-propulsion system simulation (C-MAPSS) dataset is used to simulate and verify the proposed model. Compared with Bi-LSTM and other models, the proposed model achieves higher prediction accuracy and better adaptability.