ICL4RUL: In-Context Learning-Based Aircraft Engine Remaining Useful Life Prediction

Accurately predicting the remaining useful life (RUL) of an aircraft engine is critical for enhancing aircraft reliability and safety. To address the issues of recurrent neural network (RNN)’s inability to prioritize the significance of the most contributive temporal weights and the gradient vanishing problem arising from deep training, this study proposes a novel model that integrates a multihead attention mechanism (MHA) into a residual network (ResNet) and introduces bi-directional long short-term memory (BiLSTM) with adaptive degradation temporal weighting (ADTW) module where two novel downsampling techniques are designed to predict the RUL of aircraft engines. This model, referred to as in-context learning-based aircraft engine RUL prediction (ICL4RUL), captures both temporal and spatial contextual features of an aircraft engine’s operating state. Specifically, it identifies intrinsic temporal evolution patterns in the time-series data from each sensor across different operational phases, as well as the spatial correlations among sensors located in various subsystems. As a result, the model enhances the accuracy and stability of RUL prediction through its robust ability to extract contextual patterns. Through a comparative analysis of the NASA C-MAPSS dataset, the model demonstrates superior RUL prediction performance in terms of three evaluation metrics root-mean-square error (RMSE), Score, and coefficient of determination (R2). Through ablation study and analysis of heatmaps, the advantages of ADTW and cross residual multihead attention are further demonstrated. Moreover, additional extensive experimental results on the real-world IEEE PHM2012 dataset are provided to further validate our model. The findings of this study can be effectively incorporated into the digital twin framework and Industrial Internet of Things (IIoT), optimizing decision-making processes related to aircraft engine maintenance.