A 3-D Attention-Enhanced Hybrid Neural Network for Turbofan Engine Remaining Life Prediction Using CNN and BiLSTM Models

As the most popular power source equipment in commercial aviation, turbofan engines face problems such as difficulties in data acquisition and unbalanced datasets. In addition, it is enormously challenging to construct a realistic remaining useful life (RUL) prediction model that can be applied because of the complex physical mechanisms involved in turbofan engine operation and the strong nonlinear properties of the data collected by multiple sensors. Moreover, most of the existing RUL prediction models' lack interpretability. Therefore, in this study, a 3-D attention-enhanced hybrid neural network is proposed to solve the RUL prediction problem of turbofan engines. The model employs a convolutional network to extract local features from sensor data and uses a bidirectional long short-term memory (BiLSTM) framework to learn long-dependent nonlinear features. To enhance the learning capability of the model, this study incorporates a 3-D attention module capable of balancing space and channels to visualize and analyze the action weights learned by the neural network through the attention mechanism, and the proposed attention module is found to have good robustness and generalization performance with good interpretability. In addition, handcrafted features with degradation trend coefficients and average degradation values are used as auxiliary inputs for all-round learning of the hybrid model in this study. Finally, tested on a real commercial modular aero propulsion system simulation (C-MAPSS) benchmark dataset, the proposed method in this study has the most superior performance and better practical application compared with the existing advanced RUL prediction methods.