Predicting the remaining useful life of automotive components is vital for safety and reliability in dynamic operating environments.Existing data-driven methods often miss the temporal dynamics and evolving patterns in sensor data, limiting prediction accuracy.Propose a novel simulation modelling framework that merges a physics-informed degradation simulator with a deep learning network augmented by multi-head temporal attention.This fusion generates realistic degradation trajectories while the attention mechanism dynamically prioritises key time-based features for precise life estimation.Testing on a public turbofan engine dataset shows model achieves a mean absolute error of 12.8 cycles and a root mean square error of 16.3 cycles, surpassing conventional long short-term memory and convolutional neural network models by 18.7% and 23.4%, respectively.The attention outputs provide interpretable views into critical degradation phases, offering a robust and insightful tool for prognostics and health management in automotive systems.
Li Wang (Thu,) studied this question.