基于特征增强与时空信息嵌入的涡扇发动机剩余寿命预测

针对现有的剩余寿命预测方法对原始数据利用率不高以及多维数据特征提取能力不足的问题，提出了一种基于特征增强和时空信息嵌入的卷积神经模型。首先，通过特征增强模块在原始数据基础上进一步提取工况特征与手工特征作为辅助特征；其次，提出了时空嵌入模块，对原始数据进行时空信息编码以嵌入时间序列信息和空间特征信息；最后，拼接上述特征并通过回归预测模块捕获数据内在关系得到回归预测结果。在通用的涡扇发动机模拟数据集(C-MAPSS)上对该模型预测效果进行了测试。实验结果表明，与现有主流深度学习方法相比，该模型在四个子集上的均方根误差平均减少了8.8%,且在多工况的运行条件和故障类型下，其预测精度均优于现有先进算法，充分证明了该模型在涡扇发动机剩余使用寿命预测方面的有效性和准确性。

Remaining useful life prediction of turbofan engines based on feature enhancement and spatio-temporal information embedding

To address the low utilization of raw data and insufficient feature extraction capability of multi-dimensional data in existing remaining useful life prediction methods, this paper proposed a convolutional neural network model based on feature enhancement and spatio-temporal information embedding. Firstly, it adopted a feature enhancement module to extract additional operating condition features and manual features from raw data as auxiliary features. Then, it introduced the spatio-temporal embedding module to encode the spatio-temporal information, embedding the time series information and spatial feature information into the original data. Finally, it concatenated the aforementioned features, and it employed a regression prediction module to capture the inherent relationships in the data and obtain regression prediction results. It evaluated the predictive effectiveness of the proposed model on the commonly used commercial modular aero-propulsion system simulation(C-MAPSS) dataset. The experimental results show that the root mean square error of the proposed model decreases by 8.8% on average over the four subsets compared with other mainstream deep learning methods, and it also outperforms existing state-of-the-art algorithms in prediction accuracy under multiple operating conditions and fault types. The experiments fully verify the effectiveness and accuracy of the proposed model in predicting the remaining useful life of turbofan engines.