A lightweight transformer and depthwise separable convolution model for remaining useful life prediction of turbofan engines

Abstract

The degradation of turbofan engines under complex operating conditions makes it difficult to predict their remaining useful life (RUL), which affects aircraft maintenance efficiency and reliability. To maintain prediction accuracy while improving prediction speed under the limited computing power and memory resources of edge devices, a lightweight Transformer and depthwise separable convolutional neural network (DSCformer) prediction model has been proposed. In the proposed DSCformer method, a probsparse self-attention mechanism with convolutional transformation of the Value branch is developed to improve the efficiency of dot-product, and depthwise separable convolution is employed to extract local spatiotemporal features replace the decoder in Transformer. Additionally, the model’s ability to capture overall trends is improved by incorporating a scaling factor in the Bayesian optimization algorithm, which also accelerates the search for the smoothing coefficient. The evaluation on the C-MAPSS dataset shows that the proposed method achieves a root mean square error of 11.33 and 12.44, as well as scores of 634.22 and 947.35 for predicting FD002 and FD004, respectively, within a shorter training time. These results indicate that the proposed method outperforms state-of-the-art prediction methods under multiple operating conditions for aero engine RUL prediction.