Intelligent prediction for remaining life of aero-engine --A Study on CNN-BiLSTM Model Based on Scores Loss Function

With the advent of the era of big data and artificial intelligence, predicting and managing the remaining service life of aero-engines has become a key challenge, which has a direct impact on the enhancement of autonomous control capabilities. In this paper, a new deep learning model combining a one-dimensional convolutional neural network (1D-CNN) and a bidirectional long and short-term memory network (BiLSTM) is proposed to efficiently handle dynamic and uncertain engine health state data. The concurrent model takes full advantage of 1D-CNN in extracting local features and BiLSTM in capturing time-dependence, avoiding the loss in the information transfer process in conventional models. Empirical studies on NASA's C-MAPSS dataset show that the model significantly improves the accuracy and robustness in predicting the remaining service life, reducing the RMSE by up to 5.05% and the Scores by up to 54.89% compared to the conventional model. Especially in the short-term prediction task, the model shows higher stability and accuracy, which provides strong support for advance warning in the field of aviation safety.