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OA北大核心CSTPCD

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 em-bedding module to encode the spatio-temporal information,embedding the time series information and spatial feature informa-tion 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 accu-racy of the proposed model in predicting the remaining useful life of turbofan engines.