# Residual Life Prediction of SA-CNN-BILSTM Aero-Engine Based on a Multichannel Hybrid Network
###### Abstract  
As the core component of an airplane, the health status of the aviation engine is crucial for the safe operation of the aircraft. Therefore, predicting the remaining service life of the engine is of great significance for ensuring its safety and reliability. In this paper, a multichannel hybrid network is proposed; this network is a combination of the one-dimensional convolutional neural network (1D-CNN), the bidirectional long short-term memory network (BiLSTM), and the self-attention mechanism. For each sensor of the engine, an SA-CNN-BiLSTM network is established. The one-dimensional convolutional neural network and the bidirectional long short-term memory network are used to extract the spatial features and temporal features of the input data, respectively. Moreover, multichannel modeling is utilized to achieve the parallel processing of different sensors. Subsequently, the results are stitched together to establish a mapping relationship with the engine's remaining useful life (RUL). Experimental validation was conducted on the aero-engine C-MAPSS dataset. The prediction results were compared with those of the other seven models to verify the effectiveness of this method in predicting the remaining service life. The results indicate that the proposed method significantly reduces the prediction error compared to other models. Specifically, for the two datasets, their mean absolute errors were only 11.47 and 11.76, the root-mean-square error values were only 12.26 and 12.78, and the scoring function values were only 195 and 227.  
c 2025 15, 966  
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<ccs2015> <concept>  
parameters and employs data analytics to mine effective features to establish a mapping relationship with RUL. Subsequently, this relationship can be utilized to characterize the health of an aero-engine. This approach does not require much prior knowledge and can effectively handle the nonlinear relationship between degradation states and sensor data. In data-driven approaches, deep learning-based prediction methods can directly model highly nonlinear, complex, and multidimensional systems and explore the mapping relationship between data and prediction goals. [PERSON] et al. first used convolutional neural networks to predict the remaining life of an aero-engine [5]. [PERSON] et al. utilized a combination of convolutional neural networks and long short-term memory networks to fully extract the data's spatial and time features and predicted the remaining life of an aero-engine [6]. [PERSON] et al. used deep convolutional neural networks to extract the spatial properties of data in the time dimension by implementing deep convolutional operations along the time dimension of the sensor channel [7]. [PERSON] et al. used recurrent neural networks (RNNs) to construct aero-engine health indicators and performed RUL using these health indicators [8]. [PERSON] et al. proposed kernel global principal component analysis to extract the nonlinear features of sensor data, combined with gated recurrent units, to predict the RUL of an aero-engine [9]. To evaluate the RUL of an engine under complex operating conditions, [PERSON] et al. developed an RUL prediction method based on a bidirectional long short-term memory network (Bi-LSTM) [10]. [PERSON] et al. predicted the RUL of an aero-engine using a hybrid approach combining a convolutional neural network and a bidirectional long short-term memory network, and the prediction accuracy of the hybrid model was significantly improved compared to the single model [11]. [PERSON] et al. constructed a multichannel artificial neural network (ANN) to extract multiple features of a battery, and their proposed model has a high degree of generalization [12]. [PERSON] et al. proposed a prediction model with a parallel processing method [13]. [PERSON] et al. used a two-channel hybrid model to predict the RUL of an aero-engine, and the results showed that its performance was superior to that of the single-channel prediction model [14].